@article{singh_lambeth_iyer_sharma_2024, title={Dynamic Active Subspaces for Model Predictive Allocation in Over-Actuated Systems}, volume={8}, ISSN={["2475-1456"]}, url={https://doi.org/10.1109/LCSYS.2023.3342094}, DOI={10.1109/LCSYS.2023.3342094}, abstractNote={In this letter, we analyze dynamic optimization problem for robotic systems utilizing dynamic active subspaces ( $Dy\mathcal {AS}$ ) to obtain a lower-dimensional control input space by performing a global sensitivity analysis. In doing so, we set up a Model Predictive Control Allocation (MPCA) problem wherein the actuators are dynamically allocated to track a desired stabilizing torque while satisfying state and control constraints. To improve computational efficiency of the MPCA, we develop Koopman operator-based linear prediction dynamics of an over-actuated nonlinear robotic system. We demonstrate the derived results on a hybrid neuroprosthesis model for a trajectory tracking task wherein we show a muscle fatigue-based joint torque allocation among motor and functional electrical stimulation (FES) actuators.}, journal={IEEE CONTROL SYSTEMS LETTERS}, author={Singh, Mayank and Lambeth, Krysten and Iyer, Ashwin and Sharma, Nitin}, year={2024}, pages={145–150} }
 @article{xue_wu_cai_chen_moon_huang_kim_peng_feng_sharma_et al._2024, title={Flexible Ultrasonic Transducers for Wearable Biomedical Applications: A Review on Advanced Materials, Structural Designs, and Future Prospects}, url={https://doi.org/10.1109/TUFFC.2023.3333318}, DOI={10.1109/TUFFC.2023.3333318}, abstractNote={Due to the rapid developments in materials science and fabrication techniques, wearable devices have recently received increased attention for biomedical applications, particularly in medical ultrasound imaging, sensing, and therapy. Ultrasound is ubiquitous in biomedical applications because of its non-invasive nature, nonionic radiating, high precision, and real-time capabilities. While conventional ultrasound transducers are rigid and bulky, flexible transducers can be conformed to curved body areas for continuous sensing without restricting tissue movement or transducer shifting. This article comprehensively reviews the application of flexible ultrasound transducers in the field of biomedical imaging, sensing, and therapy. First, we review the background of flexible ultrasound transducers. Following that, we discuss advanced materials and fabrication techniques for flexible ultrasound transducers and their enabling technology status. Lastly, we highlight and summarize some promising preliminary data with recent applications of flexible ultrasound transducers in biomedical imaging, sensing, and therapy. We also provide technical barriers, challenges, and future perspectives for further research and development.}, journal={IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control}, author={Xue, Xiangming and Wu, Huaiyu and Cai, Qianqian and Chen, Mengyue and Moon, Sunho and Huang, Ziping and Kim, Taeyang and Peng, Chang and Feng, Wuwei and Sharma, Nitin and et al.}, year={2024} }
 @article{iyer_singh_sharma_2023, title={Cooperative Control of a Hybrid Exoskeleton Using Optimal Time Varying Impedance Parameters During Stair Ascent}, ISSN={["2378-5861"]}, DOI={10.23919/ACC55779.2023.10156039}, abstractNote={Potentially, cooperative control of functional electrical stimulation (FES) and electric motors in a hybrid exoskeleton can perform stair ascent while adapting to a user’s locomotion. Towards this goal, it would be essential to determine the time varying impedance model parameters of each user while ensuring the stability of the closed loop system. While some previous studies address the stability problem when estimating time varying impedance model parameters, constraints on the parameters to their physiological values are not guaranteed. In this paper, we develop a model predictive control (MPC) based approach to prescribe physiologically constrained time varying stiffness and damping parameters for an impedance model. A terminal cost and controller for the stiffness and damping are designed to ensure the MPC problem is recursively feasible, satisfy physiological constraints, and is asymptotically stable. Another MPC-based cooperative control approach is then used to ensure that the knee joint follows the knee trajectory generated via the impedance model with optimized parameters. Simulations results show foot, knee joint, and impedance model tracking while allocating inputs between FES and motors during stair ascent and adequate foot clearance and placement.}, journal={2023 AMERICAN CONTROL CONFERENCE, ACC}, author={Iyer, Ashwin and Singh, Mayank and Sharma, Nitin}, year={2023}, pages={2739–2744} }
 @article{singh_sharma_2023, title={Data-driven Model Predictive Control for Drop Foot Correction}, ISSN={["2378-5861"]}, DOI={10.23919/ACC55779.2023.10156600}, abstractNote={Functional Electrical Stimulation (FES) is an effective method to restore the normal range of ankle motion in people with Drop Foot. This paper aims to develop a real-time, data-driven Model Predictive Control (MPC) scheme of FES for drop foot correction (DFC). We utilize a Koopman operator-based framework for system identification required for setting up the MPC scheme. Using the Koopman operator we can fully capture the nonlinear dynamics through an infinite dimensional linear operator describing the evolution of functions of state space. We use inertial measurement units (IMUs) for collecting the foot pitch and roll rate state information to build an approximate linear predictor for FES actuated ankle motion. In doing so, we also account for the implicit muscle actuation dynamics which are dependent on the activation and fatigue levels of the Tibialis Anterior (TA) muscle contribution during ankle motion, and hence, develop a relationship between FES input parameters and ankle motion, tailored to an individual user. The approximation, although computationally expensive, leads to reformulating the optimization problem as a quadratic program for the MPC problem. Further, we show the closed-loop system’s recursive feasibility and asymptotic stability analysis. Simulation and experimental results from a subject with Multiple Sclerosis show the effectiveness of the data-driven MPC scheme of FES for DFC.}, journal={2023 AMERICAN CONTROL CONFERENCE, ACC}, author={Singh, Mayank and Sharma, Nitin}, year={2023}, pages={2615–2620} }
 @misc{altaf_sharma_srivastava_mandal_adavi_jena_bairwa_gopalakrishnan_kumar_dey_et al._2023, title={Deciphering the melatonin-mediated response and signalling in the regulation of heavy metal stress in plants}, volume={257}, ISSN={["1432-2048"]}, DOI={10.1007/s00425-023-04146-8}, abstractNote={Melatonin has a protective effect against heavy metal stress in plants by immobilizing HM in cell walls and sequestering them in root cell vacuoles, reducing HM's translocation from roots to shoots. It enhances osmolyte production, increases antioxidant enzyme activity, and improves photosynthesis, thereby improving cellular functions. Understanding the melatonin-mediated response and signalling can sustain crop production in heavy metal-stressed soils. Melatonin is a pleiotropic signal molecule that plays a critical role in plant growth and stress tolerance, particularly against heavy metals in soil. Heavy metals (HMs) are ubiquitously found in the soil-water environment and readily taken up by plants, thereby disrupting mineral nutrient homeostasis, osmotic balance, oxidative stress, and altered primary and secondary metabolism. Plants combat HM stress through inbuilt defensive mechanisms, such as metal exclusion, restricted foliar translocation, metal sequestration and compartmentalization, chelation, and scavenging of free radicals by antioxidant enzymes. Melatonin has a protective effect against the damaging effects of HM stress in plants. It achieves this by immobilizing HM in cell walls and sequestering them in root cell vacuoles, reducing HM's translocation from roots to shoots. This mechanism improves the uptake of macronutrients and micronutrients in plants. Additionally, melatonin enhances osmolyte production, improving the plant's water relations, and increasing the activity of antioxidant enzymes to limit lipid peroxidation and reactive oxygen species (ROS) levels. Melatonin also decreases chlorophyll degradation while increasing its synthesis, and enhances RuBisCO activity for better photosynthesis. All these functions contribute to improving the cellular functions of plants exposed to HM stress. This review aims to gain better insight into the melatonin-mediated response and signalling under HM stress in plants, which may be useful in sustaining crop production in heavy metal-stressed soils.}, number={6}, journal={PLANTA}, author={Altaf, Muhammad Ahsan and Sharma, Nitin and Srivastava, Dipali and Mandal, Sayanti and Adavi, Sandeep and Jena, Rupak and Bairwa, Rakesh Kumar and Gopalakrishnan, Abilash Valsala and Kumar, Awadhesh and Dey, Abhijit and et al.}, year={2023}, month={Jun} }
 @article{xue_zhang_moon_xu_huang_sharma_jiang_2023, title={Development of a Wearable Ultrasound Transducer for Sensing Muscle Activities in Assistive Robotics Applications}, volume={13}, ISSN={["2079-6374"]}, url={https://doi.org/10.3390/bios13010134}, DOI={10.3390/bios13010134}, abstractNote={Robotic prostheses and powered exoskeletons are novel assistive robotic devices for modern medicine. Muscle activity sensing plays an important role in controlling assistive robotics devices. Most devices measure the surface electromyography (sEMG) signal for myoelectric control. However, sEMG is an integrated signal from muscle activities. It is difficult to sense muscle movements in specific small regions, particularly at different depths. Alternatively, traditional ultrasound imaging has recently been proposed to monitor muscle activity due to its ability to directly visualize superficial and at-depth muscles. Despite their advantages, traditional ultrasound probes lack wearability. In this paper, a wearable ultrasound (US) transducer, based on lead zirconate titanate (PZT) and a polyimide substrate, was developed for a muscle activity sensing demonstration. The fabricated PZT-5A elements were arranged into a 4 × 4 array and then packaged in polydimethylsiloxane (PDMS). In vitro porcine tissue experiments were carried out by generating the muscle activities artificially, and the muscle movements were detected by the proposed wearable US transducer via muscle movement imaging. Experimental results showed that all 16 elements had very similar acoustic behaviors: the averaged central frequency, −6 dB bandwidth, and electrical impedance in water were 10.59 MHz, 37.69%, and 78.41 Ω, respectively. The in vitro study successfully demonstrated the capability of monitoring local muscle activity using the prototyped wearable transducer. The findings indicate that ultrasonic sensing may be an alternative to standardize myoelectric control for assistive robotics applications.}, number={1}, journal={BIOSENSORS-BASEL}, author={Xue, Xiangming and Zhang, Bohua and Moon, Sunho and Xu, Guo-Xuan and Huang, Chih-Chung and Sharma, Nitin and Jiang, Xiaoning}, year={2023}, month={Jan} }
 @article{zhang_lambeth_sun_dodson_bao_sharma_2023, title={Evaluation of a Fused Sonomyography and Electromyography-Based Control on a Cable-Driven Ankle Exoskeleton}, volume={2}, ISSN={["1941-0468"]}, url={https://doi.org/10.1109/TRO.2023.3236958}, DOI={10.1109/TRO.2023.3236958}, abstractNote={This article presents an assist-as-needed (AAN) control framework for exoskeleton assistance based on human volitional effort prediction via a Hill-type neuromuscular model. A sequential processing algorithm-based multirate observer is applied to continuously estimate muscle activation levels by fusing surface electromyography (sEMG) and ultrasound (US) echogenicity signals from the ankle muscles. An adaptive impedance controller manipulates the exoskeleton's impedance for a more natural behavior by following a desired intrinsic impedance model. Two neural networks provide robustness to uncertainties in the overall ankle joint-exoskeleton model and the prediction error in the volitional ankle joint torque. A rigorous Lyapunov-based stability analysis proves that the AAN control framework achieves uniformly ultimately bounded tracking for the overall system. Experimental studies on five participants with no neurological disabilities walking on a treadmill validate the effectiveness of the designed ankle exoskeleton and the proposed AAN approach. Results illustrate that the AAN control approach with fused sEMG and US echogenicity signals maintained a higher human volitional effort prediction accuracy, less ankle joint trajectory tracking error, and less robotic assistance torque than the AAN approach with the sEMG-based volitional effort prediction alone. The findings support our hypotheses that the proposed controller increases human motion intent prediction accuracy, improves the exoskeleton's control performance, and boosts voluntary participation from human subjects. The new framework potentially paves a foundation for using multimodal biological signals to control rehabilitative or assistive robots.}, journal={IEEE TRANSACTIONS ON ROBOTICS}, author={Zhang, Qiang and Lambeth, Krysten and Sun, Ziyue and Dodson, Albert and Bao, Xuefeng and Sharma, Nitin}, year={2023}, month={Feb} }
 @article{xue_iyer_sharma_2023, title={Koopman-based Data-driven Model Predictive Control of Limb Tremor Dynamics with Online Model Updating: A Theoretical Modeling and Simulation Approach}, ISSN={["2378-5861"]}, DOI={10.23919/ACC55779.2023.10156240}, abstractNote={Patients suffering from tremors have difficulty performing activities of daily living. The development of a model of a limb with tremors can pave the way for non-surgical tremor suppression control techniques. Nevertheless, nonlinearity and actuator saturation make it difficult to develop an accurate model and a tremor suppression control method. Towards addressing this issue, this paper describes a Koopman-based method for system identification and its application to the design of a model predictive control (MPC) scheme to suppress tremors. Since model prediction accuracy is critical to the performance of an MPC, it is essential to update the model online if the predictions are not sufficiently accurate. We propose a recursive least squares (RLS) algorithm to improve control performance with low computational complexity. Finally, for the first time, stability analysis and recursive feasibility of the Koopman-based MPC (KMPC) closed-loop updated system are presented. The proposed modeling and control approach have been validated by experimental data and simulation results.}, journal={2023 AMERICAN CONTROL CONFERENCE, ACC}, author={Xue, Xiangming and Iyer, Ashwin and Sharma, Nitin}, year={2023}, pages={2873–2878} }
 @article{xue_iyer_roque_sharma_2023, title={Nonlinear System Identification of Tremors Dynamics: A Data-driven Approximation Using Koopman Operator Theory}, ISSN={["1948-3546"]}, DOI={10.1109/NER52421.2023.10123909}, abstractNote={People who suffer from tremors have difficulty performing activities of daily living. Efforts in developing a model of a limb with tremors can pave the way for non-surgical tremor suppression techniques. However, due to the nonlinearity, developing an accurate model of tremors is challenging. This paper implements a data-driven method for approximating the Koopman operator, which is capable of presenting nonlinear dynamics in a linear framework and is promising for predicting the nonlinear system. A dynamic model of tremors is developed with ultrasound (US) image data collected from a patient with essential tremor as they grasp objects. The method is applied to predict the patient's tremor dynamics and is compared with the nonlinear Hammerstein-Wiener system identification technique.}, journal={2023 11TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING, NER}, author={Xue, Xiangming and Iyer, Ashwin and Roque, Daniel and Sharma, Nitin}, year={2023} }
 @article{lambeth_singh_sharma_2023, title={Robust Control Barrier Functions for Safety Using a Hybrid Neuroprosthesis}, ISSN={["2378-5861"]}, DOI={10.23919/ACC55779.2023.10155862}, abstractNote={Many lower-limb hybrid neuroprostheses lack powered ankle assistance and thus cannot compensate for functional electrical stimulation-induced muscle fatigue at the ankle joint. The lack of a powered ankle joint poses a safety issue for users with foot drop who cannot volitionally clear the ground during walking. We propose zeroing control barrier functions (ZCBFs) that guarantee safe foot clearance and fatigue mitigation, provided that the trajectory begins within the prescribed safety region. We employ a backstepping-based model predictive controller (MPC) to account for activation dynamics, and we formulate a constraint to ensure the ZCBF is robust to modeling uncertainty and disturbance. Simulations show the superior performance of the proposed robust MPC-ZCBF scheme for achieving foot clearance compared to traditional ZCBFs and Euclidean safety constraints.}, journal={2023 AMERICAN CONTROL CONFERENCE, ACC}, author={Lambeth, Krysten and Singh, Mayank and Sharma, Nitin}, year={2023}, pages={54–59} }
 @article{sharma_xue_iyer_jiang_roque_2023, title={Towards ultrasound imaging-based closed-loop peripheral nerve stimulation for tremor suppression}, volume={28}, ISSN={["2468-4511"]}, url={https://doi.org/10.1016/j.cobme.2023.100484}, DOI={10.1016/j.cobme.2023.100484}, abstractNote={Despite several decades of research investigating the use of peripheral electrical stimulation (ES) for tremor suppression in an upper limb, ES design for effective tremor suppression remains elusive. The article reviews sensing approaches to measure limb tremors and existing musculoskeletal models of tremor and their use in closed-loop suppression control. We also motivate a case for incorporating ultrasound (US) imaging into the closed-loop control for increased tremor suppression efficacy. When combined with wearable US transducers, the novel approach could be a promising technique to advance musculoskeletal models that investigate tremor mechanisms and new ES closed-loop techniques with personalized stimulation parameters for tremor suppression.}, journal={CURRENT OPINION IN BIOMEDICAL ENGINEERING}, author={Sharma, Nitin and Xue, Xiangming and Iyer, Ashwin and Jiang, Xiaoning and Roque, Daniel}, year={2023}, month={Dec} }
 @article{sheng_iyer_sun_kim_sharma_2022, title={A Hybrid Knee Exoskeleton Using Real-Time Ultrasound-Based Muscle Fatigue Assessment}, volume={5}, ISSN={["1941-014X"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85130478475&partnerID=MN8TOARS}, DOI={10.1109/TMECH.2022.3171086}, abstractNote={Ultrasound-based state assessment of the human muscle during rehabilitation and its integration into a hybrid exoskeleton comprising an functional electrical stimulation (FES) system and a powered orthosis are emerging research areas. This article presents results from the first experimental demonstration of a hybrid knee exoskeleton that uses ultrasound-derived muscle state feedback to coordinate electrical motors and FES. A significant contribution of the article is to integrate a real-time ultrasound image acquisition and processing framework into a recently derived switching-based feedback control of the hybrid knee exoskeleton. As a result, the contractility response of the quadriceps muscle to the FES input can be monitored in vivo in real-time and estimate FES-induced muscle fatigue changes in the muscle. The switched controller’s decision-making process can then use the estimated muscle fatigue to compensate or replace the FES-stimulated muscle power with an electrical motor, thus avoiding extensive stimulation of the fatigued muscle. The experimental results suggest a potential application in the rehabilitation of neurological disorders like spinal cord injuries and stroke.}, number={4}, journal={IEEE-ASME TRANSACTIONS ON MECHATRONICS}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Sheng, Zhiyu and Iyer, Ashwin and Sun, Ziyue and Kim, Kang and Sharma, Nitin}, year={2022}, month={May} }
 @article{molazadeh_zhang_bao_sharma_2022, title={An Iterative Learning Controller for a Switched Cooperative Allocation Strategy During Sit-to-Stand Tasks with a Hybrid Exoskeleton}, volume={30}, ISSN={["1558-0865"]}, url={https://doi.org/10.1109/TCST.2021.3089885}, DOI={10.1109/TCST.2021.3089885}, abstractNote={A hybrid exoskeleton that combines functional electrical stimulation (FES) and a powered exoskeleton is an emerging technology for assisting people with mobility disorders. The cooperative use of FES and the exoskeleton allows active muscle contractions through FES while robustifying torque generation to reduce FES-induced muscle fatigue. In this article, a switched distribution of allocation ratios between FES and electric motors in a closed-loop adaptive control design is explored for the first time. The new controller uses an iterative learning neural network (NN)-based control law to compensate for structured and unstructured parametric uncertainties in the hybrid exoskeleton model. A discrete Lyapunov-like stability analysis that uses a common energy function proves asymptotic stability for the switched system with iterative learning update laws. Five human participants, including a person with complete spinal cord injury, performed sit-to-stand tasks with the new controller. The experimental results showed that the synthesized controller, in a few iterations, reduced the root mean square error between desired positions and actual positions of the knee and hip joints by 46.20% and 53.34%, respectively. The sit-to-stand experimental results also show that the proposed NN-based iterative learning control (NNILC) approach can recover the asymptotically trajectory tracking performance despite the switching of allocation levels between FES and electric motor. Compared to a proportional-derivative controller and traditional iterative learning control, the findings showed that the new controller can potentially simplify the clinical implementation of the hybrid exoskeleton with minimal parameters tuning.}, number={3}, journal={IEEE Transactions on Control Systems Technology}, publisher={IEEE}, author={Molazadeh, V. and Zhang, Q. and Bao, X. and Sharma, N.}, year={2022}, month={May}, pages={1021–1036} }
 @article{sun_qiu_iyer_dicianno_sharma_2022, title={Continuous Switching Control of an Input-Delayed Antagonistic Muscle Pair During Functional Electrical Stimulation}, volume={6}, ISSN={["1558-0865"]}, url={https://doi.org/10.1109/TCST.2022.3178935}, DOI={10.1109/TCST.2022.3178935}, abstractNote={Existing controllers for functional electrical stimulation (FES) of upper limb muscles were initially designed to assist unilateral movements and may not be readily applicable to assist antagonistic muscle movements. Furthermore, it is yet unclear if electromechanical delays (EMDs) are present during the coactivation of muscles. In this article, a robust controller is designed to facilitate the FES of an antagonistic muscle pair during elbow flexion and extension. The controller uses a continuous switching law that maps a joint angle error to control the antagonistic muscle pair. Furthermore, the controller compensates for EMDs in the antagonistic muscle pair. A Lyapunov stability analysis yields uniformly ultimately bounded (UUB) tracking for the human limb joint. The experimental results on four participants without disabilities indicate that the controller is robust and effective in switching between antagonistic muscles. A separate set of experiments also showed that EMDs are indeed present in the coactivated muscle pair. The designed controller compensates for the EMDs and statistically improves root mean square error (RMSE) compared to a traditional linear controller with no EMD compensation. The proposed controller can be generalized to assist FES-elicited tasks that involve a weak antagonistic muscle pair.}, journal={IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Sun, Ziyue and Qiu, Tianyi and Iyer, Ashwin and Dicianno, Brad E. and Sharma, Nitin}, year={2022}, month={Jun} }
 @article{xue_zhang_moon_xu_huang_sharma_jiang_2022, title={Development of a wearable ultrasound transducer for sensing muscle activities in assistive robotics applications: In vivo study}, ISSN={["1948-5719"]}, DOI={10.1109/IUS54386.2022.9958535}, abstractNote={People who suffer from the amputation of limbs or with mobility impairment due to methodological disorder sometimes require assistive robotics (AR), such as robotic prostheses and exoskeletons, to function satisfactorily and productively in daily life. Dynamic measurements of muscle voluntary activities are widely used to control AR, and sensors used to control AR should be non-invasive, effective, and wearable. Ultrasound (US) imaging is an effective method for measuring muscle activity. Nevertheless, conventional US transducers are cumbersome and inflexible, making them inconvenient for continuous monitoring of muscle activity for AR control. In light of no report available about using a flexible transducer for detecting muscle activities for AR, this work aims to develop a novel wearable US device for detecting muscle activities. In specific, a 16-element 10 MHz flexible sparse array was designed, fabricated, and characterized. The feasibility of monitoring muscle activity in different regions was demonstrated by an in vivo human experiment.}, journal={2022 IEEE INTERNATIONAL ULTRASONICS SYMPOSIUM (IEEE IUS)}, author={Xue, Xiangming and Zhang, Bohua and Moon, Sunho and Xu, Guo-Xuan and Huang, Chih-Chung and Sharma, Nitin and Jiang, Xiaoning}, year={2022} }
 @article{bulea_sharma_sikdar_su_2022, title={Editorial: Next Generation User-Adaptive Wearable Robots}, volume={9}, ISSN={["2296-9144"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85133809356&partnerID=MN8TOARS}, DOI={10.3389/frobt.2022.920655}, abstractNote={One study presents a pediatric exoskeleton that provides adaptive assistance to knee extension to alleviate crouch and its evaluation in a child with cerebral palsy (Chen et al.). Two manuscripts present novel controllers which leverage reinforcement learning and their evaluation in simulation: one for assisting squatting motion (Luo et al.) and one for bipedal exoskeleton walking in three dimensions (Liu et al.). The fi nal manuscript evaluates the fusion of surface electromyography (EMG) and muscle sonography to estimate limb movement in a variety of locomotor tasks (Rabe and Fey). a novel Motor Assisted Hybrid Neuroprosthesis (MAHNP) with actuated hip and knee joints and a distributed control architecture that integrates the exoskeleton with customized FES systems. A supervisory gait event detector split the gait cycle into four discrete states. The hip and/or knee motors could be activated with bursts of torque to assist the stimulation-driven limb motion. The system was evaluated in two participants with SCI, each with different implanted stimulation systems. Each}, journal={FRONTIERS IN ROBOTICS AND AI}, author={Bulea, Thomas C. and Sharma, Nitin and Sikdar, Siddhartha and Su, Hao}, year={2022}, month={Jun} }
 @article{zhang_fragnito_franz_sharma_2022, title={Fused Ultrasound And Electromyography-Driven Neuromuscular Model To Improve Plantarflexion Moment Prediction Across Walking Speeds}, url={https://doi.org/10.21203/rs.3.rs-1136552/v1}, DOI={10.21203/rs.3.rs-1136552/v1}, abstractNote={Abstract Background: Improving the prediction ability of a human-machine interface (HMI) is critical to accomplish a bio-inspired or model-based control strategy for rehabilitation interventions, which are of increased interest to assist limb function post neurological injuries. A fundamental role of the HMI is to accurately predict human intent by mapping signals from a mechanical sensor or surface electromyography (sEMG) sensor. These sensors are limited to measuring the resulting limb force or movement or the neural signal evoking the force. As the intermediate mapping in the HMI also depends on muscle contractility, a motivation exists to include architectural features of the muscle as surrogates of dynamic muscle movement, thus further improving the HMI's prediction accuracy. Objective: The purpose of this study is to investigate a non-invasive sEMG and ultrasound (US) imaging-driven Hill-type neuromuscular model (HNM) for net ankle joint plantarflexion moment prediction. We hypothesize that the fusion of signals from sEMG and US imaging results in a more accurate net plantarflexion moment prediction than sole sEMG or US imaging. Methods: Ten young non-disabled participants walked on a treadmill at speeds of 0.50, 0.75, 1.00, 1.25, and 1.50 m/s. The proposed HNM consists of two muscle-tendon units. The muscle activation for each unit was calculated as a weighted summation of the normalized sEMG signal and normalized muscle thickness signal from US imaging. The HNM calibration was performed under both single-speed mode and inter-speed mode, and then the calibrated HNM was validated across all walking speeds. Results: On average, the normalized moment prediction root mean square error was reduced by 14.58 % (p = 0.012) and 36.79 % (p < 0.001) with the proposed HNM when compared to sEMG-driven and US imaging-driven HNMs, respectively. Also, the calibrated models with data from the inter-speed mode were more robust than those from single-speed modes for the moment prediction. Conclusions: The proposed sEMG-US imaging-driven HNM can significantly improve the net plantarflexion moment prediction accuracy across multiple walking speeds. The findings imply that the proposed HNM can be potentially used in bio-inspired control strategies for rehabilitative devices due to its superior prediction.}, author={Zhang, Qiang and Fragnito, Natalie and Franz, Jason R. and Sharma, Nitin}, year={2022}, month={Jan} }
 @article{zhang_fragnito_franz_sharma_2022, title={Fused Ultrasound and Electromyography-driven Neuromuscular Model to Improve Plantarflexion Moment Prediction across Walking Speeds}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85132172249&partnerID=MN8TOARS}, DOI={10.21203/rs.3.rs-1136552}, journal={ResearchSquare}, author={Zhang, Q. and Fragnito, N. and Franz, J.R. and Sharma, N.}, year={2022} }
 @article{zhang_fragnito_franz_sharma_2022, title={Fused ultrasound and electromyography-driven neuromuscular model to improve plantarflexion moment prediction across walking speeds}, volume={19}, url={https://doi.org/10.1186/s12984-022-01061-z}, DOI={10.1186/s12984-022-01061-z}, abstractNote={Improving the prediction ability of a human-machine interface (HMI) is critical to accomplish a bio-inspired or model-based control strategy for rehabilitation interventions, which are of increased interest to assist limb function post neurological injuries. A fundamental role of the HMI is to accurately predict human intent by mapping signals from a mechanical sensor or surface electromyography (sEMG) sensor. These sensors are limited to measuring the resulting limb force or movement or the neural signal evoking the force. As the intermediate mapping in the HMI also depends on muscle contractility, a motivation exists to include architectural features of the muscle as surrogates of dynamic muscle movement, thus further improving the HMI's prediction accuracy.The purpose of this study is to investigate a non-invasive sEMG and ultrasound (US) imaging-driven Hill-type neuromuscular model (HNM) for net ankle joint plantarflexion moment prediction. We hypothesize that the fusion of signals from sEMG and US imaging results in a more accurate net plantarflexion moment prediction than sole sEMG or US imaging.Ten young non-disabled participants walked on a treadmill at speeds of 0.50, 0.75, 1.00, 1.25, and 1.50 m/s. The proposed HNM consists of two muscle-tendon units. The muscle activation for each unit was calculated as a weighted summation of the normalized sEMG signal and normalized muscle thickness signal from US imaging. The HNM calibration was performed under both single-speed mode and inter-speed mode, and then the calibrated HNM was validated across all walking speeds.On average, the normalized moment prediction root mean square error was reduced by 14.58 % ([Formula: see text]) and 36.79 % ([Formula: see text]) with the proposed HNM when compared to sEMG-driven and US imaging-driven HNMs, respectively. Also, the calibrated models with data from the inter-speed mode were more robust than those from single-speed modes for the moment prediction.The proposed sEMG-US imaging-driven HNM can significantly improve the net plantarflexion moment prediction accuracy across multiple walking speeds. The findings imply that the proposed HNM can be potentially used in bio-inspired control strategies for rehabilitative devices due to its superior prediction.}, number={1}, journal={Journal of NeuroEngineering and Rehabilitation}, author={Zhang, Qiang and Fragnito, Natalie and Franz, Jason R. and Sharma, Nitin}, year={2022}, month={Aug} }
 @article{zhang_clark_franz_sharma_2022, title={Personalized fusion of ultrasound and electromyography-derived neuromuscular features increases prediction accuracy of ankle moment during plantarflexion}, volume={71}, ISSN={["1746-8108"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85114124714&partnerID=MN8TOARS}, DOI={10.1016/j.bspc.2021.103100}, abstractNote={Compared to mechanical signals that are used for estimating human limb motion intention, non-invasive surface electromyography (sEMG) is a preferred signal in human-robotic systems. However, noise interference, crosstalk from adjacent muscle groups, and an inability to measure deeper muscle tissues are disadvantageous to sEMG’s reliable use. In this work, we hypothesize that a fusion between sEMG and in vivo ultrasound (US) imaging will result in more accurate detection of ankle movement intention. Nine young able-bodied participants were included to volitionally perform isometric plantarflexion tasks with different fixed-end ankle postures, while the sEMG and US imaging data of plantarflexors were synchronously collected. We created three dominant feature sets, sole sEMG feature set, sole US feature set, and sEMG-US feature fusion set, to calibrate and validate a support vector machine regression model (SVR) and a feedforward neural network model (FFNN) with labeled net moment measurements. The results showed that, compared to the sole sEMG feature set, the sEMG-US fusion set reduced the average net moment prediction error by 35.7% (p < 0.05), when using SVR, and by 21.5% (p < 0.05), when using FFNN. In SVR, the sole US feature set reduced the prediction error by 24.9% (p < 0.05) when compared to the sole sEMG feature set. In FFNN, the sEMG-US fusion set reduced the prediction error by 28.2% (p < 0.05) when compared to the sole US feature set. These findings indicate that the combination of sEMG signals and US imaging is a superior sensing modality for predicting human plantarflexion intention and can enable future clinical rehabilitation devices.}, journal={BIOMEDICAL SIGNAL PROCESSING AND CONTROL}, author={Zhang, Qiang and Clark, William H. and Franz, Jason R. and Sharma, Nitin}, year={2022}, month={Jan} }
 @article{zhang_iyer_lambeth_kim_sharma_2022, title={Ultrasound Echogenicity as an Indicator of Muscle Fatigue during Functional Electrical Stimulation}, volume={22}, ISSN={["1424-8220"]}, url={https://www.mdpi.com/1424-8220/22/1/335}, DOI={10.3390/s22010335}, abstractNote={Functional electrical stimulation (FES) is a potential neurorehabilitative intervention to enable functional movements in persons with neurological conditions that cause mobility impairments. However, the quick onset of muscle fatigue during FES is a significant challenge for sustaining the desired functional movements for more extended periods. Therefore, a considerable interest still exists in the development of sensing techniques that reliably measure FES-induced muscle fatigue. This study proposes to use ultrasound (US) imaging-derived echogenicity signal as an indicator of FES-induced muscle fatigue. We hypothesized that the US-derived echogenicity signal is sensitive to FES-induced muscle fatigue under isometric and dynamic muscle contraction conditions. Eight non-disabled participants participated in the experiments, where FES electrodes were applied on their tibialis anterior (TA) muscles. During a fatigue protocol under either isometric and dynamic ankle dorsiflexion conditions, we synchronously collected the isometric dorsiflexion torque or dynamic dorsiflexion angle on the ankle joint, US echogenicity signals from TA muscle, and the applied stimulation intensity. The experimental results showed an exponential reduction in the US echogenicity relative change (ERC) as the fatigue progressed under the isometric (R2=0.891±0.081) and dynamic (R2=0.858±0.065) conditions. The experimental results also implied a strong linear relationship between US ERC and TA muscle fatigue benchmark (dorsiflexion torque or angle amplitude), with R2 values of 0.840±0.054 and 0.794±0.065 under isometric and dynamic conditions, respectively. The findings in this study indicate that the US echogenicity signal is a computationally efficient signal that strongly represents FES-induced muscle fatigue. Its potential real-time implementation to detect fatigue can facilitate an FES closed-loop controller design that considers the FES-induced muscle fatigue.}, number={1}, journal={SENSORS}, author={Zhang, Qiang and Iyer, Ashwin and Lambeth, Krysten and Kim, Kang and Sharma, Nitin}, year={2022}, month={Jan} }
 @article{zhang_lambeth_iyer_sun_sharma_2022, title={Ultrasound Imaging-Based Closed-Loop Control of Functional Electrical Stimulation for Drop Foot Correction}, volume={9}, ISSN={["1558-0865"]}, url={https://doi.org/10.1109/TCST.2022.3207999}, DOI={10.1109/TCST.2022.3207999}, abstractNote={Open- or closed-loop functional electrical stimulation (FES) has been widely investigated to treat drop foot syndrome, which is typically caused by weakness or paralysis of ankle dorsiflexors. However, conventional closed-loop FES control mainly uses kinematic feedback, which does not directly capture time-varying changes in muscle activation. In this study, we explored the use of ultrasound (US) echogenicity as an indicator of FES-evoked muscle activation and hypothesized that including US-derived muscle activation, in addition to kinematic feedback, would improve the closed-loop FES control performance compared to the closed-loop control that relies only on the kinematic feedback. A sampled-data observer (SDO) was derived to continuously estimate FES-evoked muscle activations from low-sampled US echogenicity signals. In addition, a dynamic surface controller (DSC) and a delay compensation (DC) term were incorporated with the SDO, denoted as the US-based DSC-DC, to drive the actual ankle dorsiflexion trajectory to the desired profile. The trajectory tracking error convergence of the closed-loop system was proven to be uniformly ultimately bounded based on the Lyapunov–Krasovskii stability analysis. The US-based DSC-DC controller was validated on five participants with no disabilities to control their ankle dorsiflexion during walking on a treadmill. The US-based DSC-DC controller significantly reduced the root-mean-square error of the ankle joint trajectory tracking by 46.52% ± 7.99% ( $p < 0.001$ ) compared to the traditional DSC-DC controller with only kinematic feedback but no US measurements. The results also verified the disturbance rejection performance of the US-based DSC-DC controller when a plantarflexion disturbance was added. Our control design, for the first time, provides a methodology to integrate US in an FES control framework, which will likely benefit persons with drop foot and those with other mobility disorders.}, journal={IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY}, author={Zhang, Qiang and Lambeth, Krysten and Iyer, Ashwin and Sun, Ziyue and Sharma, Nitin}, year={2022}, month={Sep} }
 @inbook{zhang_iyer_sharma_2022, title={Ultrasound-Based Sensing and Control of Functional Electrical Stimulation for Ankle Joint Dorsiflexion: Preliminary Study}, volume={27}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85109560445&partnerID=MN8TOARS}, DOI={10.1007/978-3-030-69547-7_50}, abstractNote={Functional electrical stimulation (FES) is a potential technique for reanimating paralyzed muscles post neurological injury/disease. Several technical challenges, including the difficulty in measuring FES-induced muscle activation and muscle fatigue, and compensating for the electromechanical delay (EMD) during muscle force generation, inhibit its satisfactory control performance. In this paper, an ultrasound (US) imaging approach is proposed to observe muscle activation and fatigue levels during FES-elicited ankle dorsiflexors. Due to the low sampling rate of the US imaging-derived signal, a sampled-data observer (SDO) is designed to continuously estimate the muscle activation and fatigue based on their continuous dynamics. The SDO is combined with a delay compensation term to address the ankle dorsiflexion trajectory tracking problem with a known input delay. Experimental results on an able-bodied participant show the effectiveness of the proposed control method, and the superior tracking performance compared to a traditional control method, where the muscle activation and fatigue are computed from an off-line identified model.}, booktitle={Biosystems and Biorobotics}, author={Zhang, Q. and Iyer, A. and Sharma, N.}, year={2022}, pages={307–311} }
 @article{zhang_iyer_sun_kim_sharma_2021, title={A Dual-Modal Approach Using Electromyography and Sonomyography Improves Prediction of Dynamic Ankle Movement: A Case Study}, volume={29}, ISSN={["1558-0210"]}, url={https://doi.org/10.1109/TNSRE.2021.3106900}, DOI={10.1109/TNSRE.2021.3106900}, abstractNote={For decades, surface electromyography (sEMG) has been a popular non-invasive bio-sensing technology for predicting human joint motion. However, cross-talk, interference from adjacent muscles, and its inability to measure deeply located muscles limit its performance in predicting joint motion. Recently, ultrasound (US) imaging has been proposed as an alternative non-invasive technology to predict joint movement due to its high signal-to-noise ratio, direct visualization of targeted tissue, and ability to access deep-seated muscles. This paper proposes a dual-modal approach that combines US imaging and sEMG for predicting volitional dynamic ankle dorsiflexion movement. Three feature sets: 1) a uni-modal set with four sEMG features, 2) a uni-modal set with four US imaging features, and 3) a dual-modal set with four dominant sEMG and US imaging features, together with measured ankle dorsiflexion angles, were used to train multiple machine learning regression models. The experimental results from a seated posture and five walking trials at different speeds, ranging from 0.50 m/s to 1.50 m/s, showed that the dual-modal set significantly reduced the prediction root mean square errors (RMSEs). Compared to the uni-modal sEMG feature set, the dual-modal set reduced RMSEs by up to 47.84% for the seated posture and up to 77.72% for the walking trials. Similarly, when compared to the US imaging feature set, the dual-modal set reduced RMSEs by up to 53.95% for the seated posture and up to 58.39% for the walking trials. The findings show that potentially the dual-modal sensing approach can be used as a superior sensing modality to predict human intent of a continuous motion and implemented for volitional control of clinical rehabilitative and assistive devices.}, journal={IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Zhang, Qiang and Iyer, Ashwin and Sun, Ziyue and Kim, Kang and Sharma, Nitin}, year={2021}, pages={1944–1954} }
 @inproceedings{sun_bao_zhang_lambeth_sharma_2021, title={A Tube-based Model Predictive Control Method for Joint Angle Tracking with Functional Electrical Stimulation and An Electric Motor Assist}, volume={2021-May}, ISBN={9781665441971}, url={http://dx.doi.org/10.23919/acc50511.2021.9483084}, DOI={10.23919/acc50511.2021.9483084}, abstractNote={During functional electrical stimulation (FES), muscle force saturation and a user's tolerance levels of stimulation intensity limit a controller's ability to deliver the desired amount of stimulation, which, if unaddressed, degrade the performance of high-gain feedback control strategies. Additionally, these strategies may overstimulate the muscles, which further contribute to the rapid onset of muscle fatigue. Cooperative control of FES with an electric motor assist may allow stimulation levels within the imposed limits, reduce overall stimulation duty cycle, and compensate for the muscle fatigue. Model predictive controller (MPC) is one such optimal control strategy to achieve these control objectives of the combined hybrid system. However, the traditional MPC method for the hybrid system requires exact model knowledge of the dynamic system, i.e., cannot handle modeling uncertainties, and the recursive feasibility has been shown only for limb regulation problems. So far, extending the current results to a limb tracking problem has been challenging. In this paper, a novel tube-based MPC method for tracking control of a human limb angle by cooperatively using FES and electric motor inputs is derived. A feedback controller for the electrical motor assist is designed such that it reduces the error between the nominal MPC and the output of the actual hybrid system. Further, a terminal controller and terminal constraint region are derived to show the recursive feasibility of the robust MPC scheme. Simulation results were performed on a single degree of freedom knee extension model. The results show robust performance despite modeling uncertainties.}, booktitle={2021 American Control Conference (ACC)}, publisher={IEEE}, author={Sun, Ziyue and Bao, Xuefeng and Zhang, Qiang and Lambeth, Krysten and Sharma, Nitin}, year={2021}, month={May}, pages={1390–1395} }
 @article{bao_sheng_dicianno_sharma_2021, title={A Tube-based Model Predictive Control Method to Regulate a Knee Joint with Functional Electrical Stimulation and Electric Motor Assist}, volume={29}, ISSN={["1558-0865"]}, url={https://doi.org/10.1109/TCST.2020.3034850}, DOI={10.1109/TCST.2020.3034850}, abstractNote={A hybrid neuroprosthesis system is a promising rehabilitation technology to restore lower limb function in persons with paraplegia. The technology combines functional electrical stimulation (FES) and a powered lower limb exoskeleton to produce movements for walking and standing. The main control challenge in the hybrid neuroprosthesis is to achieve an optimal coordination between FES and electric motors. Model-based optimal control methods have been suggested for the control of the hybrid neuroprosthesis. However, it is often difficult to effect robust control performance with model-based optimal control methods due to modeling uncertainties. A tube-based model predictive control (MPC) method is developed to obtain robust and optimal coordination between FES and an electric motor during a knee regulation task. An external feedback control is used to limit the error between the actual position and the MPC-computed nominal position. The tube-based MPC method is proved to have recursive feasibility, compliance to input constraints, and exponentially bounded stability. The experimental results obtained from an able-bodied participant and a participant with spinal cord injury validate the controller’s ability to allocate control inputs to FES and the electric motor as well as method’s robustness to modeling uncertainties.}, number={5}, journal={IEEE Transactions on Control Systems Technology}, publisher={IEEE}, author={Bao, X. and Sheng, Z. and Dicianno, B. and Sharma, N.}, year={2021}, month={Sep}, pages={2180–2191} }
 @article{zhang_iyer_kim_sharma_2021, title={Evaluation of Non-Invasive Ankle Joint Effort Prediction Methods for Use in Neurorehabilitation Using Electromyography and Ultrasound Imaging}, volume={68}, ISSN={["1558-2531"]}, url={https://doi.org/10.1109/TBME.2020.3014861}, DOI={10.1109/TBME.2020.3014861}, abstractNote={Objective: Reliable measurement of voluntary human effort is essential for effective and safe interaction between the wearer and an assistive robot. Existing voluntary effort prediction methods that use surface electromyography (sEMG) are susceptible to prediction inaccuracies due to non-selectivity in measuring muscle responses. This technical challenge motivates an investigation into alternative non-invasive effort prediction methods that directly visualize the muscle response and improve effort prediction accuracy. The paper is a comparative study of ultrasound imaging (US)-derived neuromuscular signals and sEMG signals for their use in predicting isometric ankle dorsiflexion moment. Furthermore, the study evaluates the prediction accuracy of model-based and model-free voluntary effort prediction approaches that use these signals. Methods: The study evaluates sEMG signals and three US imaging-derived signals: pennation angle, muscle fascicle length, and echogenicity and three voluntary effort prediction methods: linear regression (LR), feedforward neural network (FFNN), and Hill-type neuromuscular model (HNM). Results: In all the prediction methods, pennation angle and fascicle length significantly improve the prediction accuracy of dorsiflexion moment, when compared to echogenicity. Also, compared to LR, both FFNN and HNM improve dorsiflexion moment prediction accuracy. Conclusion: The findings indicate FFNN or HNM approach and using pennation angle or fascicle length predict human ankle movement intent with higher accuracy. Significance: The accurate ankle effort prediction will pave the path to safe and reliable robotic assistance in patients with drop foot.}, number={3}, journal={IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Zhang, Qiang and Iyer, Ashwin and Kim, Kang and Sharma, Nitin}, year={2021}, month={Mar}, pages={1044–1055} }
 @inproceedings{zhang_fragnito_myers_sharma_2021, title={Plantarflexion Moment Prediction during the Walking Stance Phase with an sEMG-Ultrasound Imaging-Driven Model}, volume={2021-January}, ISSN={["1558-4615"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85122533499&partnerID=MN8TOARS}, DOI={10.1109/EMBC46164.2021.9630046}, abstractNote={Many rehabilitative exoskeletons use non-invasive surface electromyography (sEMG) to measure human volitional intent. However, signals from adjacent muscle groups interfere with sEMG measurements. Further, the inability to measure sEMG signals from deeply located muscles may not accurately measure the volitional intent. In this work, we combined sEMG and ultrasound (US) imaging-derived signals to improve the prediction accuracy of voluntary ankle effort. We used a multivariate linear model (MLM) that combines sEMG and US signals for ankle joint net plantarflexion (PF) moment prediction during the walking stance phase. We hypothesized that the proposed sEMG-US imaging-driven MLM would result in more accurate net PF moment prediction than sEMG-driven and US imaging-driven MLMs. Synchronous measurements including reflective makers coordinates, ground reaction forces, sEMG signals of lateral/medial gastrocnemius (LGS/MGS), and soleus (SOL) muscles, and US imaging of LGS and SOL muscles were collected from five able-bodied participants walking on a treadmill at multiple speeds. The ankle joint net PF moment benchmark was calculated based on inverse dynamics, while the net PF moment prediction was determined by the sEMG-US imaging-driven, sEMG-driven, and US imaging-driven MLMs. The findings show that the sEMG-US imaging-driven MLM can significantly improve the prediction of net PF moment during the walking stance phase at multiple speeds. Potentially, the proposed sEMG-US imaging-driven MLM can be used as a superior joint motion intent model in advanced and intelligent control strategies for rehabilitative exoskeletons.}, booktitle={2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)}, author={Zhang, Q. and Fragnito, N. and Myers, A. and Sharma, N.}, year={2021}, month={Nov}, pages={6267–6272} }
 @article{molazadeh_zhang_bao_dicianno_sharma_2021, title={Shared Control of a Powered Exoskeleton and Functional Electrical Stimulation Using Iterative Learning}, volume={8}, ISSN={["2296-9144"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85119421007&partnerID=MN8TOARS}, DOI={10.3389/frobt.2021.711388}, abstractNote={A hybrid exoskeleton comprising a powered exoskeleton and functional electrical stimulation (FES) is a promising technology for restoration of standing and walking functions after a neurological injury. Its shared control remains challenging due to the need to optimally distribute joint torques among FES and the powered exoskeleton while compensating for the FES-induced muscle fatigue and ensuring performance despite highly nonlinear and uncertain skeletal muscle behavior. This study develops a bi-level hierarchical control design for shared control of a powered exoskeleton and FES to overcome these challenges. A higher-level neural network–based iterative learning controller (NNILC) is derived to generate torques needed to drive the hybrid system. Then, a low-level model predictive control (MPC)-based allocation strategy optimally distributes the torque contributions between FES and the exoskeleton’s knee motors based on the muscle fatigue and recovery characteristics of a participant’s quadriceps muscles. A Lyapunov-like stability analysis proves global asymptotic tracking of state-dependent desired joint trajectories. The experimental results on four non-disabled participants validate the effectiveness of the proposed NNILC-MPC framework. The root mean square error (RMSE) of the knee joint and the hip joint was reduced by 71.96 and 74.57%, respectively, in the fourth iteration compared to the RMSE in the 1st sit-to-stand iteration.}, journal={FRONTIERS IN ROBOTICS AND AI}, author={Molazadeh, Vahidreza and Zhang, Qiang and Bao, Xuefeng and Dicianno, Brad E. and Sharma, Nitin}, year={2021}, month={Nov} }
 @article{sheng_sun_molazadeh_sharma_2021, title={Switched Control of an N-Degree-of-Freedom Input Delayed Wearable Robotic System}, volume={125}, ISSN={["1873-2836"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85099482130&partnerID=MN8TOARS}, DOI={10.1016/j.automatica.2020.109455}, abstractNote={In this paper, a switched control method for a class of wearable robotic systems that prioritizes the use of human skeletal muscles in an assistive rigid powered exoskeleton is derived. A general N-degree-of-freedom (N-DOF) human–robot model is proposed to consider the challenges induced by the wearable system that include uncertainties and nonlinearities, unilateral actuation properties of the skeletal muscles, input delays, as well as a time varying actuator efficiency. Two control modes that alternatively switch and control a wearable robotic system are designed to overcome these challenges. A multiple Lyapunov functional analysis with state-dependent constraints on the switch criteria is performed to prove the stability. Simulations are performed to demonstrate the gain conditions, selected for each subsystem, that stabilize the overall system. Experiments on a human participant wearing a 4-DOF hybrid exoskeleton that combines functional electrical stimulation and a powered exoskeleton demonstrate the effectiveness of the switched control design.}, journal={Automatica}, publisher={Elsevier ScienceDirect}, author={Sheng, Z. and Sun, Z. and Molazadeh, V. and Sharma, N.}, year={2021}, month={Mar} }
 @article{sheng_sharma_kim_2021, title={Ultra-High-Frame-Rate Ultrasound Monitoring of Muscle Contractility Changes Due to Neuromuscular Electrical Stimulation}, volume={49}, ISSN={["1573-9686"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85085878244&partnerID=MN8TOARS}, DOI={10.1007/s10439-020-02536-7}, abstractNote={The quick onset of muscle fatigue is a critical issue when applying neuromuscular electrical stimulation (NMES) to generate muscle contractions for functional limb movements, which were lost/impaired due to a neurological disorder or an injury. For in situ assessment of the effect of NMES-induced muscle fatigue, a novel noninvasive sensor modality that can quantify the degraded contractility of a targeted muscle is required. In this study, instantaneous strain maps of a contracting muscle were derived from ultra-high-frame-rate (2 kHz) ultrasound images to quantify the contractility. A correlation between strain maps and isometric contraction force values was investigated. When the muscle reached its maximum contraction, the maximum and the mean values of the strain map were correlated with the force values and were further used to stage the contractility change. During the muscle activation period, a novel methodology based on the principal component regression (PCR) was proposed to explore the strain-force correlation. The quadriceps muscle of 3 able-bodied human participants was investigated during NMES-elicited isometric knee extension experiments. Strong to very strong correlation results were obtained and indicate that the proposed measurements from ultrasound images are promising to quantify the muscle contractility changes during NMES.}, number={1}, journal={ANNALS OF BIOMEDICAL ENGINEERING}, author={Sheng, Zhiyu and Sharma, Nitin and Kim, Kang}, year={2021}, month={Jan}, pages={262–275} }
 @inproceedings{zhang_iyer_lambeth_kim_sharma_2021, title={Ultrasound Echogenicity-based Assessment of Muscle Fatigue During Functional Electrical Stimulation}, ISSN={["1558-4615"]}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85122094226&partnerID=MN8TOARS}, DOI={10.1109/EMBC46164.2021.9630325}, abstractNote={The rapid onset of muscle fatigue during functional electrical stimulation (FES) is a major challenge when attempting to perform long-term periodic tasks such as walking. Surface electromyography (sEMG) is frequently used to detect muscle fatigue for both volitional and FES-evoked muscle contraction. However, sEMG contamination from both FES stimulation artifacts and residual M-wave signals requires sophisticated processing to get clean signals and evaluate the muscle fatigue level. The objective of this paper is to investigate the feasibility of computationally efficient ultrasound (US) echogenicity as a candidate indicator of FES-induced muscle fatigue. We conducted isometric and dynamic ankle dorsiflexion experiments with electrically stimulated tibialis anterior (TA) muscle on three human participants. During a fatigue protocol, we synchronously recorded isometric dorsiflexion force, dynamic dorsiflexion angle, US images, and stimulation intensity. The temporal US echogenicity from US images was calculated based on a gray-scaled analysis to assess the decrease in dorsiflexion force or motion range due to FES-induced TA muscle fatigue. The results showed a monotonic reduction in US echogenicity change along with the fatigue progression for both isometric (R2 =0.870±0.026) and dynamic (R2 =0.803±0.048) ankle dorsiflexion. These results implied a strong linear relationship between US echogenicity and TA muscle fatigue level. The findings indicate that US echogenicity may be a promising computationally efficient indicator for assessing FES-induced muscle fatigue and may aid in the design of muscle-in-the-loop FES controllers that consider the onset of muscle fatigue.}, booktitle={2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)}, author={Zhang, Q. and Iyer, A. and Lambeth, K. and Kim, K. and Sharma, N.}, year={2021}, month={Nov}, pages={5948–5952} }
 @inproceedings{sheng_kim_sharma_2020, title={An Ultrasound Imaging Based Observer for Estimating NMES-Induced Muscle Fatigue: Theory and Simulation}, volume={1}, ISBN={9780791884270}, url={http://dx.doi.org/10.1115/dscc2020-3196}, DOI={10.1115/dscc2020-3196}, abstractNote={
 Neuroprosthetic devices that use transcutaneous neuromuscular electrical stimulation (NMES) are potential interventions to restore skeletal muscle function in people with neurological disorders. As commonly noted, how to assess the NMES-induced muscle fatigue is a critical problem. This is because the capability of fatigue assessment is a necessary precursor for optimally modulating the NMES dosage to improve the control performance of a neuroprosthesis and ensure user’s safety. To effectively estimate the NMES-induced muscle fatigue, this paper proposes a novel state observer that combines a mathematical predictive fatigue model and intermittent feedback from ultrasound-derived strain images. The strain images quantify muscle contractility during NMES. Principal component regression (PCR) is used to derive a relationship between the strain images and instantaneous muscle force production. Lyapunov stability analysis was performed to obtain the convergence property of the designed observer. A globally uniformly ultimately bounded (GUUB) result was obtained. Simulations based on pre-recorded data from a human experiment were also conducted to demonstrate the performance of the designed observer.}, booktitle={Volume 1: Adaptive/Intelligent Sys. Control; Driver Assistance/Autonomous Tech.; Control Design Methods; Nonlinear Control; Robotics; Assistive/Rehabilitation Devices; Biomedical/Neural Systems; Building Energy Systems; Connected Vehicle Systems; Control/Estimation of Energy Systems; Control Apps.; Smart Buildings/Microgrids; Education; Human-Robot Systems; Soft Mechatronics/Robotic Components/Systems; Energy/Power Systems; Energy Storage; Estimation/Identification; Vehicle Efficiency/Emissions}, publisher={American Society of Mechanical Engineers}, author={Sheng, Zhiyu and Kim, Kang and Sharma, Nitin}, year={2020}, month={Oct} }
 @inproceedings{iyer_sheng_zhang_kim_sharma_2020, place={Hoboken, NJ}, title={Analysis of Tremor During Grasp Using Ultrasound Imaging: Preliminary Study}, volume={2020-November}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85095593827&partnerID=MN8TOARS}, DOI={10.1109/BioRob49111.2020.9224446}, abstractNote={This paper investigates the use of ultrasound imaging to characterize tremor during a grasping motion. Ultrasound images were collected from three human participants including an able-bodied participant, a patient with Parkinson’s disease, and a patient with essential tremor. Each human participant was instructed to grasp and hold objects with three different masses in a vertical upright position with an ultrasound probe strapped to their forearm while seated. The images were processed using an ultrasound speckle tracking algorithm to measure muscle strain during the grasping and holding motion. Analysis of the computed strain values showed marked differences in the strain peaks and frequencies between able-bodied participant and the patients with tremor. The detected frequencies depict how the strain measurement changes during the grasping and holding motion. The frequency for tremor participants fall within accepted frequency ranges for Parkinson’s Disease and Essential Tremor, and thus can be representative of the actual tremor frequency.}, booktitle={Proceedings of the 8th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics}, publisher={IEEE}, author={Iyer, A. and Sheng, Z. and Zhang, Q. and Kim, K. and Sharma, N.}, year={2020}, pages={533–538} }
 @inbook{moreno_mohammed_sharma_del-ama_2020, title={Hybrid Wearable Robotic Exoskeletons for Human Walking}, ISBN={9780128146590}, url={http://dx.doi.org/10.1016/b978-0-12-814659-0.00018-7}, DOI={10.1016/b978-0-12-814659-0.00018-7}, abstractNote={Abstract Impaired human walking can be supported or retrained by wearable robotic exoskeletons acting in parallel to the body structures. In a variety of application scenarios, muscle force can be also artificially generated by means of transcutaneous electrical stimulation. A combination of neuromuscular stimulation and wearable robots known as hybrid robotic exoskeletons brings together the potential of motor neuroprosthesis in adequate integration with smart exoskeletons. Diverse methods have been proposed for optimizing the efficiency of artificially compensated walking in the spinal cord injured. This chapter aims at revising in detail the recent advances in such hybrid robotic technology as well as new perspectives on assistive and neurorehabilitation applications. We provide an updated overview of the technology and its perspectives with regard to real use as well as directions for future research in this field. A case study is presented discussing a general framework to coordinate functional electrical stimulation (FES) of multiple gait-governing muscles with electric motors under a muscle synergy-inspired control approach.}, booktitle={Wearable Robotics}, publisher={Elsevier}, author={Moreno, Juan C. and Mohammed, Samer and Sharma, Nitin and del-Ama, Antonio J.}, year={2020}, pages={347–364} }
 @inbook{bao_molazadeh_dodson_sharma_2020, place={Cham, Switzerland}, title={Model Predictive Control-Based Knee Actuator Allocation During a Standing-Up Motion with a Powered Exoskeleton and Functional Electrical Stimulation}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85091052456&partnerID=MN8TOARS}, DOI={10.1007/978-3-030-38740-2_6}, booktitle={Advances in Motor Neuroprostheses}, publisher={Springer}, author={Bao, X. and Molazadeh, V. and Dodson, A. and Sharma, N.}, editor={Vinjamuri, R.Editor}, year={2020}, pages={89–100} }
 @article{zhang_kim_sharma_2020, title={Prediction of Ankle Dorsiflexion Moment by Combined Ultrasound Sonography and Electromyography}, volume={28}, ISSN={["1558-0210"]}, url={https://doi.org/10.1109/TNSRE.2019.2953588}, DOI={10.1109/TNSRE.2019.2953588}, abstractNote={To provide an effective and safe therapy to persons with neurological impairments, accurate determination of their residual volitional ability is required. However, accurate measurement of the volitional ability, through non-invasive means (e.g., electromyography), is challenging due to signal interference from neighboring muscles or stimulation artifacts caused by functional electrical stimulation (FES). In this work, a new model-based intention detection method that combines signals from both surface electromyography (sEMG) and ultrasound (US) sonography to predict isometric volitional ankle dorsiflexion moment is proposed. The work is motivated by the fact that the US-derived signals, unlike sEMG, provide direct visualization of the muscle activity, and hence may enhance the prediction accuracy of the volitional ability, when combined with sEMG. The weighted summation of sEMG and US imaging signals, measured on the tibialis anterior muscle, is utilized as an input to a modified Hill-type neuromusculoskeletal model that predicts the ankle dorsiflexion moment. The effectiveness of the proposed model-based moment prediction method is validated by comparing the predicted and the measured ankle joint moments. The new modeling method has a better prediction accuracy compared to a prediction model that uses sole sEMG or sole US sonography. This finding provides a more accurate approach to detect movement intent in the lower limbs. The approach can be potentially beneficial for the development of US sonography-based robotic or FES-assisted rehabilitation devices.}, number={1}, journal={IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Zhang, Qiang and Kim, Kang and Sharma, Nitin}, year={2020}, month={Jan}, pages={318–327} }
 @article{sheng_sharma_kim_2020, title={Quantitative Assessment of Changes in Muscle Contractility Due to Fatigue During NMES: An Ultrasound Imaging Approach}, volume={67}, url={https://doi.org/10.1109/TBME.2019.2921754}, DOI={10.1109/TBME.2019.2921754}, abstractNote={Objective: This paper investigates an ultrasound imaging-based non-invasive methodology to quantitatively assess changes in muscle contractility due to the fatigue induced by neuromuscular electrical stimulation (NMES). Methods: Knee extension experiments on human participants were conducted to record synchronized isometric knee force data and ultrasound images of the electrically stimulated quadriceps muscle. The data were first collected in a pre-fatigue stage and then in a post-fatigue stage. Ultrasound images were processed using a contraction rate adaptive speckle tracking algorithm. A two-dimensional strain measure field was constructed based on the muscle displacement tracking results to quantify muscle contractility. Results: Analysis of the strain images showed that, between the pre-fatigue and post-fatigue stages, there was a reduction in the strain peaks, a change in the strain peak distribution, and a decrease in an area occupied by the large positive strain. Conclusion: The results indicate changes in muscle contractility due to the NMES-induced muscle fatigue. Significance: Ultrasound imaging with the proposed methodology is a promising tool for a direct NMES-induced fatigue assessment and facilitates new strategies to alleviate the effects of the NMES-induced fatigue.}, number={3}, journal={IEEE Transactions on Biomedical Engineering}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Sheng, Zhiyu and Sharma, Nitin and Kim, Kang}, year={2020}, month={Mar}, pages={832–841} }
 @inproceedings{zhang_iyer_sun_dodson_sharma_2020, title={Sampled-Data Observer Based Dynamic Surface Control of Delayed Neuromuscular Functional Electrical Stimulation}, volume={1}, ISBN={9780791884270}, url={http://dx.doi.org/10.1115/dscc2020-3225}, DOI={10.1115/dscc2020-3225}, abstractNote={
 Functional electrical stimulation (FES) is a potential technique for reanimating paralyzed muscles post neurological injury/disease. Several technical challenges including difficulty in measuring and compensating for delayed muscle activation levels inhibit its satisfactory control performance. In this paper, an ultrasound (US) imaging approach is proposed to measure delayed muscle activation levels under the implementation of FES. Due to low sampling rates of US imaging, a sampled-data observer (SDO) is designed to estimate the muscle activation in a continuous manner. The SDO is combined with continuous-time dynamic surface control (DSC) approach that compensates for the electromechanical delay (EMD) in the tibialis anterior (TA) activation dynamics. The stability analysis based on the Lyapunov-Krasovskii function proves that the SDO-based DSC plus delay compensation (SDO-DSC-DC) approach achieves semi-global uniformly ultimately bounded (SGUUB) tracking performance. Simulation results on an ankle dorsiflexion neuromusculoskeletal system show the root mean square error (RMSE) of desired trajectory tracking is reduced by 19.77 % by using the proposed SDO-DSC-DC compared to the DSC-DC without the SDO. The findings provide potentials for rehabilitative devices, like powered exoskeleton and FES, to assist or enhance human limb movement based on the corresponding muscle activities in real-time.}, number={DSCC2020-3225DSCC2020-3225}, booktitle={Volume 1: Adaptive/Intelligent Sys. Control; Driver Assistance/Autonomous Tech.; Control Design Methods; Nonlinear Control; Robotics; Assistive/Rehabilitation Devices; Biomedical/Neural Systems; Building Energy Systems; Connected Vehicle Systems; Control/Estimation of Energy Systems; Control Apps.; Smart Buildings/Microgrids; Education; Human-Robot Systems; Soft Mechatronics/Robotic Components/Systems; Energy/Power Systems; Energy Storage; Estimation/Identification; Vehicle Efficiency/Emissions}, publisher={American Society of Mechanical Engineers}, author={Zhang, Qiang and Iyer, Ashwin and Sun, Ziyue and Dodson, Albert and Sharma, Nitin}, year={2020}, month={Oct} }
 @article{bao_molazadeh_dodson_dicianno_sharma_2020, title={Using Person-Specific Muscle Fatigue Characteristics to Optimally Allocate Control in a Hybrid Exoskeleton—Preliminary Results}, volume={2}, url={https://doi.org/10.1109/TMRB.2020.2977416}, DOI={10.1109/TMRB.2020.2977416}, abstractNote={Currently controllers that dynamically modulate functional electrical stimulation (FES) and a powered exoskeleton at the same time during standing-up movements are largely unavailable. In this paper, an optimal shared control of FES and a powered exoskeleton is designed to perform sitting to standing (STS) movements with a hybrid exoskeleton. A hierarchical control design is proposed to overcome the difficulties associated with developing an optimal real-time solution for the highly nonlinear and uncertain STS control model with multiple degrees of freedom. A higher-level robust nonlinear control design is derived to exponentially track a time-invariant desired STS movement profile. Then, a lower-level optimal control allocator is designed to distribute control between FES and the knee electric motors. The allocator uses a person’s muscle fatigue and recovery dynamics to determine an optimal ratio between the FES-elicited knee torque and the exoskeleton assist. Experiments were performed on human participants, two persons without disability and one person with spinal cord injury (SCI), to validate the feedback controller and the optimal torque allocator. The muscles of the participant with SCI did not actively contract to FES, so he was only tested with the powered exoskeleton controller. The experimental results show that the proposed hierarchical control design is a promising method to effect shared control in a hybrid exoskeleton.}, number={2}, journal={IEEE Transactions on Medical Robotics and Bionics}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Bao, Xuefeng and Molazadeh, Vahidreza and Dodson, Albert and Dicianno, Brad E. and Sharma, Nitin}, year={2020}, month={May}, pages={226–235} }
 @inproceedings{zhang_iyer_kim_sharma_2020, title={Volitional Contractility Assessment of Plantar Flexors by Using Non-invasive Neuromuscular Measurements}, volume={2020-November}, ISBN={9781728159072}, url={http://dx.doi.org/10.1109/biorob49111.2020.9224298}, DOI={10.1109/biorob49111.2020.9224298}, abstractNote={This paper investigates an ultrasound (US) imaging-based methodology to assess the contraction levels of plantar flexors quantitatively. Echogenicity derived from US imaging at different anatomical depths, including both lateral gastrocnemius (LGS) and soleus (SOL) muscles, is used for the prediction of the volitional isometric plantar flexion moment. Synchronous measurements, including a plantar flexion torque signal, a surface electromyography (sEMG) signal, and US imaging of both LGS and SOL muscles, are collected. Four feature sets, including sole sEMG, sole LGS echogenicity, sole SOL echogenicity, and their fusion, are used to train a Gaussian process regression (GPR) model and predict plantar flexion torque. The experimental results on four non-disabled participants show that the torque prediction accuracy is improved significantly by using the LGS or SOL echogenicity signal than using the sEMG signal. However, there is no significant improvement by using the fused feature compared to sole LGS or SOL echogenicity. The findings imply that using US imagingderived signals improves the accuracy of predicting volitional effort on human plantar flexors. Potentially, US imaging can be used as a new sensing modality to measure or predict human lower limb motion intent in clinical rehabilitation devices.}, booktitle={2020 8th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob)}, publisher={IEEE}, author={Zhang, Qiang and Iyer, Ashwin and Kim, Kang and Sharma, Nitin}, year={2020}, month={Nov}, pages={515–520} }
 @article{molazadeh_sheng_bao_sharma_2019, title={A Robust Iterative Learning Switching Controller for following Virtual Constraints: Application to a Hybrid Neuroprosthesis}, volume={51}, ISSN={2405-8963}, url={http://dx.doi.org/10.1016/j.ifacol.2019.01.011}, DOI={10.1016/j.ifacol.2019.01.011}, abstractNote={In this paper, a robust iterative learning switching controller that uses optimal virtual constraint is designed for a hybrid walking exoskeleton that uses functional electrical stimulation and a powered exoskeleton. The synthesis of iterative learning control with sliding-mode control improves tracking performance and accuracy. The motivation for designing this switching controller was to obtain joint torques either from functional electrical stimulation or electric motor. A generalized switching controller is utilized to switch based on the stimulated muscle fatigue state. For achieving stability in walking cycle, the controller is used to force the system to follow the designed virtual constraints. The combination of sequential quadratic programming and genetic-particle swarm optimization algorithm is used for deriving the virtual constraints. The effectiveness of the new iterative learning control for output tracking is verified in a simple model of walking (3-link) that has active actuation at the hip joints.}, number={34}, journal={IFAC-PapersOnLine}, publisher={Elsevier BV}, author={Molazadeh, Vahidreza and Sheng, Zhiyu and Bao, Xuefeng and Sharma, Nitin}, year={2019}, pages={28–33} }
 @inproceedings{zhang_sheng_moore-clingenpeel_kim_sharma_2019, place={Hoboken, NJ}, title={Ankle Dorsiflexion Strength Monitoring by Combining Sonomyography and Electromyography}, volume={2019-June}, ISBN={9781728127552}, url={http://dx.doi.org/10.1109/icorr.2019.8779530}, DOI={10.1109/icorr.2019.8779530}, abstractNote={Ankle dorsiflexion produced by Tibialis Anterior (TA) muscle contraction plays a significant role during human walking and standing balance. The weakened function or dysfunction of the TA muscle often impedes activities of daily living (ADL). Powered ankle exoskeleton is a prevalent technique to treat this pathology, and its intelligent and effective behaviors depend on human intention detection. A TA muscle contraction strength monitor is proposed to evaluate the weakness of the ankle dorsiflexion. The new method combines surface electromyography (sEMG) signals and sonomyography signals to estimate ankle torque during a voluntary isometric ankle dorsiflexion. Changes in the pennation angle (PA) are derived from the sonomyography signals. The results demonstrate strong correlations among the sonomyography-derived PA, the sEMG signal, and the measured TA muscle contraction force. Especially, the TA muscle strength monitor approximates the TA muscle strength measurement via a weighted summation of the sEMG signal and the PA signal. The new method shows an improved linear correlation with the muscle strength, compared to the correlations between the muscle strength and sole sEMG signal or sole PA signal, where the R-squared values are improved by 4.21 % and 1.99 %, respectively.}, booktitle={2019 IEEE 16th International Conference on Rehabilitation Robotics (ICORR)}, publisher={IEEE}, author={Zhang, Qiang and Sheng, Zhiyu and Moore-Clingenpeel, Frank and Kim, Kang and Sharma, Nitin}, year={2019}, month={Jun}, pages={240–245} }
 @article{sun_bao_sharma_2019, title={Lyapunov-based Model Predictive Control of an Input Delayed Functional Electrical Simulation}, volume={51}, ISSN={2405-8963}, url={http://dx.doi.org/10.1016/j.ifacol.2019.01.037}, DOI={10.1016/j.ifacol.2019.01.037}, abstractNote={In this paper a Lyapunov-based model predictive control (LMPC) method to control knee extension during an input-delayed neuromuscular electrical stimulation is developed. This method incorporates a contractive constraint under a delay compensation control law that achieves system stability despite an unknown constant input delay and imperfectly estimated model parameters The simulations were performed to compare the LMPC method with the delay compensation control law. Robustness of the LMPC method and the boundedness of the LMPC inputs are depicted.}, number={34}, journal={IFAC-PapersOnLine}, publisher={Elsevier BV}, author={Sun, Ziyue and Bao, Xuefeng and Sharma, Nitin}, year={2019}, pages={290–295} }
 @article{bao_kirsch_dodson_sharma_2019, title={Model Predictive Control of a Feedback-Linearized Hybrid Neuroprosthetic System With a Barrier Penalty}, volume={14}, ISSN={1555-1415 1555-1423}, url={http://dx.doi.org/10.1115/1.4042903}, DOI={10.1115/1.4042903}, abstractNote={Functional electrical stimulation (FES) is prescribed as a treatment to restore motor function in individuals with neurological impairments. However, the rapid onset of FES-induced muscle fatigue significantly limits its duration of use and limb movement quality. In this paper, an electric motor-assist is proposed to alleviate the fatigue effects by sharing work load with FES. A model predictive control (MPC) method is used to allocate control inputs to FES and the electric motor. To reduce the computational load, the dynamics is feedback linearized so that the nominal model inside the MPC method becomes linear. The state variables: the angular position and the muscle fatigue are still preserved in the transformed state space to keep the optimization meaningful. Because after feedback linearization the original linear input constraints may become nonlinear and state-dependent, a barrier cost function is used to overcome this issue. The simulation results show a satisfactory control performance and a reduction in the computation due to the linearization.}, number={10}, journal={Journal of Computational and Nonlinear Dynamics}, publisher={ASME International}, author={Bao, Xuefeng and Kirsch, Nicholas and Dodson, Albert and Sharma, Nitin}, year={2019}, month={Sep} }
 @inproceedings{sheng_sharma_kim_2019, title={Muscle Fatigue Assessment in a Wearable Neuroprosthesis Using Ultrasound Strain Imaging}, author={Sheng, Z. and Sharma, N. and Kim, K}, year={2019}, month={Jun} }
 @inproceedings{molazadeh_zhang_bao_sharma_2019, title={Neural-Network Based Iterative Learning Control of a Hybrid Exoskeleton with an MPC Allocation Strategy}, volume={1}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85076504457&partnerID=MN8TOARS}, DOI={10.1115/DSCC2019-9191}, abstractNote={In this paper, a novel neural network based iterative learning controller for a hybrid exoskeleton is presented. The control allocation between functional electrical stimulation and knee electric motors uses a model predictive control strategy. Further to address modeling uncertainties, the controller identifies the system dynamics and input gain matrix with neural networks in an iterative fashion. Virtual constraints are employed so that the system can use a time invariant manifold to determine desired joint angles. Simulation results show that the controller stabilizes the hybrid system for sitting to standing and standing to sitting scenarios.}, number={DSCC2019-9191DSCC2019-9191}, booktitle={Proceedings of the ASME Dynamic Systems Control Conference}, author={Molazadeh, V. and Zhang, Q. and Bao, X. and Sharma, N.}, year={2019}, month={Oct} }
 @misc{sharma_2019, title={New Control and Sensing Approaches for Integrating Functional Electrical Stimulation in a Wearable Exoskeletons}, author={Sharma, N.}, year={2019}, month={Feb} }
 @inproceedings{zhang_sheng_kim_sharma_2019, title={Observer Design for a Nonlinear Neuromuscular System with Multi-rate Sampled and Delayed Output Measurements}, volume={2019-July}, ISBN={9781538679265}, url={http://dx.doi.org/10.23919/acc.2019.8814473}, DOI={10.23919/acc.2019.8814473}, abstractNote={Robotic devices and functional electrical stimulation (FES) are utilized to provide rehabilitation therapy to persons with incomplete spinal cord injury. The goal of the therapy is to improve their weakened voluntary muscle strength. A variety of control strategies used in these therapies need a measure of participant's volitional strength. This informs the robotic or an FES device to modulate assistance proportional to the user's weakness. In this paper we propose an observer design to estimate ankle kinematics that are elicited volitionally. The observer uses a nonlinear continuous-time neuromuscular system, which has multi-rate sampled output measurements with non-uniform and unknown delays from various sensing modalities including electromyography, ultrasound imaging, and an inertial measurement unit. We assume an allowable maximum value of unsynchronized sampling intervals and nonuniform delays. By constructing a Lyapunov-Krasovskii function, sufficient conditions are derived to prove the exponential stability of the estimation error. Numerical simulations are provided to verify the effectiveness of the designed observer.}, booktitle={2019 American Control Conference (ACC)}, publisher={IEEE}, author={Zhang, Qiang and Sheng, Zhiyu and Kim, Kang and Sharma, Nitin}, year={2019}, month={Jul}, pages={872–877} }
 @article{bao_mao_munro_sun_sharma_2019, title={Sub-optimally solving actuator redundancy in a hybrid neuroprosthetic system with a multi-layer neural network structure}, volume={3}, url={https://doi.org/10.1007/s41315-019-00100-8}, DOI={10.1007/s41315-019-00100-8}, abstractNote={Functional electrical stimulation (FES) has recently been proposed as a supplementary torque assist in lower-limb powered exoskeletons for persons with paraplegia. In the combined system, also known as a hybrid neuroprosthesis, both FES-assist and the exoskeleton act to generate lower-limb torques to achieve standing and walking functions. Due to this actuator redundancy, we are motivated to optimally allocate FES-assist and exoskeleton torque based on a performance index that penalizes FES overuse to minimize muscle fatigue while also minimizing regulation or tracking errors. Traditional optimal control approaches need a system model to optimize; however, it is often difficult to formulate a musculoskeletal model that accurately predicts muscle responses due to FES. In this paper, we use a novel identification and control structure that contains a recurrent neural network (RNN) and several feedforward neural networks (FNNs). The RNN is trained by supervised learning to identify the system dynamics, while the FNNs are trained by a reinforcement learning method to provide sub-optimal control actions. The output layer of each FNN has its unique activation functions, so that the asymmetric constraint of FES and the symmetric constraint of exoskeleton motor control input can be realized. This new structure is experimentally validated on a seated human participant using a single joint hybrid neuroprosthesis.}, number={3}, journal={International Journal of Intelligent Robotics and Applications}, publisher={Springer Science and Business Media LLC}, author={Bao, Xuefeng and Mao, Zhi-Hong and Munro, Paul and Sun, Ziyue and Sharma, Nitin}, year={2019}, month={Sep}, pages={298–313} }
 @inproceedings{sun_bao_sharma_2019, title={Tube-based Model Predictive Control of An Input Delayed Functional Electrical Stimulation}, volume={2019-July}, ISBN={9781538679265}, url={http://dx.doi.org/10.23919/acc.2019.8815302}, DOI={10.23919/acc.2019.8815302}, abstractNote={Functional electrical stimulation (FES) is an external application of electrical currents to elicit muscle contractions that can potentially restore limb function in persons with spinal cord injury. However, FES often leads to the rapid onset of muscle fatigue, which limits performance of FES-based devices due to reduction in force generation capability. Fatigue is caused by unnatural muscle recruitment and synchronous and repetitive recruitment of muscle fibers. In this situation, over-stimulation of the muscle fibers further aggravates the muscle fatigue. Therefore, a motivation exists to use optimal controls that minimize muscle stimulation while providing a desired performance. Model predictive controller (MPC) is one such optimal control method. However, the traditional MPC is dependent on exact model knowledge of the musculoskeletal dynamics and cannot handle modeling uncertainties. Motivated to address modeling uncertainties, robust MPC approach is used to control FES. A new robust MPC technique is studied to address electromechanical delay (EMD) during FES, which often causes performance issues and stability problems. This paper developed a novel tube-based MPC for controlling knee extension elicited through FES. In the tube-based MPC, the EMD compensation controller was chosen to be the tube that reduced the error between the nominal MPC and the output of the real system. Regulation experiments were performed on an able-bodied participant, and the controller showed robust performance despite modeling uncertainties.}, booktitle={2019 American Control Conference (ACC)}, publisher={IEEE}, author={Sun, Ziyue and Bao, Xuefeng and Sharma, Nitin}, year={2019}, month={Jul}, pages={5420–5425} }
 @article{chen_liu_xiao_sharma_cho_kim_2019, title={Ultrasound Tracking of the Acoustically Actuated Microswimmer}, volume={66}, url={https://doi.org/10.1109/TBME.2019.2902523}, DOI={10.1109/TBME.2019.2902523}, abstractNote={Objective: The purpose of this paper is to demonstrate the ultrasound tracking strategy for the acoustically actuated bubble-based microswimmer. Methods: The ultrasound tracking performance is evaluated by comparing the tracking results with the camera tracking. A benchtop experiment is conducted to capture the motion of two types of microswimmers by synchronized ultrasound and camera systems. A laboratory developed tracking algorithm is utilized to estimate the trajectory for both tracking methods. Results: The trajectory reconstructed from ultrasound tracking method compares well with the conventional camera tracking, exhibiting a high accuracy and robustness for three different types of moving trajectories. Conclusion: Ultrasound tracking is an accurate and reliable approach to track the motion of the acoustically actuated microswimmers. Significance: Ultrasound imaging is a promising candidate for noninvasively tracking the motion of microswimmers inside the body in biomedical applications and may further promote the real-time control strategy for the microswimmers.}, number={11}, journal={IEEE Transactions on Biomedical Engineering}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Chen, Qiyang and Liu, Fang-Wei and Xiao, Zunding and Sharma, Nitin and Cho, Sung Kwon and Kim, Kang}, year={2019}, month={Nov}, pages={3231–3237} }
 @article{alibeji_molazadeh_dicianno_sharma_2018, title={A Control Scheme That Uses Dynamic Postural Synergies to Coordinate a Hybrid Walking Neuroprosthesis: Theory and Experiments}, volume={12}, ISSN={1662-453X}, url={http://dx.doi.org/10.3389/fnins.2018.00159}, DOI={10.3389/fnins.2018.00159}, abstractNote={A hybrid walking neuroprosthesis that combines functional electrical stimulation (FES) with a powered lower limb exoskeleton can be used to restore walking in persons with paraplegia. It provides therapeutic benefits of FES and torque reliability of the powered exoskeleton. Moreover, by harnessing metabolic power of muscles via FES, the hybrid combination has a potential to lower power consumption and reduce actuator size in the powered exoskeleton. Its control design, however, must overcome the challenges of actuator redundancy due to the combined use of FES and electric motor. Further, dynamic disturbances such as electromechanical delay (EMD) and muscle fatigue must be considered during the control design process. This ensures stability and control performance despite disparate dynamics of FES and electric motor. In this paper, a general framework to coordinate FES of multiple gait-governing muscles with electric motors is presented. A muscle synergy-inspired control framework is used to derive the controller and is motivated mainly to address the actuator redundancy issue. Dynamic postural synergies between FES of the muscles and the electric motors were artificially generated through optimizations and result in key dynamic postures when activated. These synergies were used in the feedforward path of the control system. A dynamic surface control technique, modified with a delay compensation term, is used as the feedback controller to address model uncertainty, the cascaded muscle activation dynamics, and EMD. To address muscle fatigue, the stimulation levels in the feedforward path were gradually increased based on a model-based fatigue estimate. A Lyapunov-based stability approach was used to derive the controller and guarantee its stability. The synergy-based controller was demonstrated experimentally on an able-bodied subject and person with an incomplete spinal cord injury.}, number={APR}, journal={Frontiers in Neuroscience}, publisher={Frontiers Media SA}, author={Alibeji, Naji A. and Molazadeh, Vahidreza and Dicianno, Brad E. and Sharma, Nitin}, year={2018}, month={Apr} }
 @article{alibeji_molazadeh_moore-clingenpeel_sharma_2018, title={A Muscle Synergy-Inspired Control Design to Coordinate Functional Electrical Stimulation and a Powered Exoskeleton: Artificial Generation of Synergies to Reduce Input Dimensionality}, volume={38}, ISSN={1066-033X 1941-000X}, url={http://dx.doi.org/10.1109/mcs.2018.2866603}, DOI={10.1109/mcs.2018.2866603}, abstractNote={Mobility disorders caused by spinal cord injury (SCI), stroke, or progressive neurological diseases such as multiple sclerosis and amyotrophic lateral sclerosis, lead to a deterioration in quality of life. Resulting sequelae, such as pressure ulcers, depression, and urinary infections, require constant medical care throughout a patient's lifetime. Evidence has shown that, following an injury or a disease, individuals who use rehabilitative interventions to restore walking and standing functions experience fewer secondary medical complications than do wheelchair users [1]. Two such rehabilitative interventions include functional electrical stimulation (FES) and powered exoskeletons. These technologies have the potential to mitigate secondary health complications, lower medical expenses, and achieve independent ambulation in individuals following an SCI.}, number={6}, journal={IEEE Control Systems}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Alibeji, Naji A. and Molazadeh, Vahidreza and Moore-Clingenpeel, Frank and Sharma, Nitin}, year={2018}, month={Dec}, pages={35–60} }
 @inproceedings{molazadeh_sheng_sharma_2018, title={A Within-Stride Switching Controller for Walking with Virtual Constraints: Application to a Hybrid Neuroprosthesis}, volume={2018-June}, ISBN={9781538654286}, url={http://dx.doi.org/10.23919/acc.2018.8431436}, DOI={10.23919/acc.2018.8431436}, abstractNote={In this paper, we investigated if a switching controller and optimal virtual constraint design for a class of walking exoskeleton with redundant actuation can achieve a stable walking limit cycle. The motivation was to address control allocation problem in a hybrid walking neuroprosthesis that can obtain joint torques either from electric motor or functional electrical stimulation. A generalized switching controller is designed to switch between the motor and the muscle stimulation based on the fatigue state of the stimulated muscle. The controller is used to meet the virtual constraints designed to achieve a stable walking cycle and is proven to achieve exponential stability despite arbitrary switching. The virtual constraints were optimized based on the results of combination of genetic-particle swarm optimization algorithm and SQP method. A simple model of walking (3-link) that has active actuation at the hip joints was simulated to depict the controller feasibility.}, booktitle={2018 Annual American Control Conference (ACC)}, publisher={IEEE}, author={Molazadeh, Vahidreza and Sheng, Zhiyu and Sharma, Nitin}, year={2018}, month={Jun}, pages={5286–5291} }
 @article{bao_mao_sharma_2018, title={A supplementary condition for the convergence of the control policy during adaptive dynamic programming}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85098840725&partnerID=MN8TOARS}, journal={arXiv}, author={Bao, X. and Mao, Z.-H. and Sharma, N.}, year={2018} }
 @article{bao_mao_sharma_2018, title={A theoretical difficulty in approximate dynamic programming with input constraints}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-85098820021&partnerID=MN8TOARS}, journal={arXiv}, author={Bao, X. and Mao, Z.-H. and Sharma, N.}, year={2018} }
 @inproceedings{moore-clingenpeel_molazadeh_sharma_2018, place={Hoboken, NJ}, title={An Active-Subspace-Based Algorithm for Reducing Redundancy in a Hybrid Neuroprosthesis}, volume={2018-December}, ISBN={9781538613955}, url={http://dx.doi.org/10.1109/cdc.2018.8619032}, DOI={10.1109/cdc.2018.8619032}, abstractNote={Hybrid neuroprosthesis (HN) systems combine the use of a powered exoskeleton with functional electrical stimulation of muscles to restore mobility in persons with paraplegia. At the same time, HN systems suffer from increased complexity due to redundant actuators that require coordination to function effectively. Muscle synergy-inspired control that coordinates these actuators can reduce system complexity. The calculation of these synergies requires the solution of a constrained optimization problem. As a way to reduce complexity during dynamic optimizations, we have developed a novel technique based on active subspaces to reduce the dimensionality of the redundant control system. Given an initial system state, a set of random control trajectories were generated, and the gradient of the cost function was obtained through the derivation of an adjoint function. The active subspaces were then obtained by performing eigenvalue decomposition on the outer product of the gradient of the cost function, and choosing the appropriate eigenvectors based on the magnitude of their corresponding eigenvalues. By leveraging the algorithm's parallel nature as well as simplifications that can be utilized in adjoint calculation, we show that by using this algorithm, synergies can be calculated more quickly than performing dynamic optimization. Once the active subspace is found, it is used in a gradient-projection optimization scheme to control the redundant actuators.}, booktitle={2018 IEEE Conference on Decision and Control (CDC)}, publisher={IEEE}, author={Moore-Clingenpeel, Frank and Molazadeh, Vahidreza and Sharma, Nitin}, year={2018}, month={Dec}, pages={4849–4854} }
 @article{doll_kirsch_bao_dicianno_sharma_2018, title={Dynamic optimization of stimulation frequency to reduce isometric muscle fatigue using a modified Hill‐Huxley model}, volume={57}, ISSN={0148-639X 1097-4598}, url={http://dx.doi.org/10.1002/mus.25777}, DOI={10.1002/mus.25777}, abstractNote={Optimal frequency modulation during functional electrical stimulation (FES) may minimize or delay the onset of FES‐induced muscle fatigue.}, number={4}, journal={Muscle & Nerve}, publisher={Wiley}, author={Doll, Brian D. and Kirsch, Nicholas A. and Bao, Xuefeng and Dicianno, Brad E. and Sharma, Nitin}, year={2018}, month={Apr}, pages={634–641} }
 @inproceedings{sheng_molazadeh_sharma_2018, title={Hybrid Dynamical System Model and Robust Control of a Hybrid Neuroprosthesis Under Fatigue Based Switching}, volume={2018-June}, ISBN={9781538654286}, url={http://dx.doi.org/10.23919/acc.2018.8431258}, DOI={10.23919/acc.2018.8431258}, abstractNote={A hybrid neuroprosthesis controller that integrates a modified PD-based robust controller to compensate for electromechanical delay during functional electrical stimulation and a variable structure controller to control a powered exoskeleton is developed. A hybrid dynamical systems approach is used to model the hybrid neuroprosthesis control and prove the stability. Simulation results demonstrate the model and the control design during a standing task.}, booktitle={2018 Annual American Control Conference (ACC)}, publisher={IEEE}, author={Sheng, Zhiyu and Molazadeh, Vahidreza and Sharma, Nitin}, year={2018}, month={Jun}, pages={1446–1451} }
 @article{kirsch_bao_alibeji_dicianno_sharma_2018, title={Model-Based Dynamic Control Allocation in a Hybrid Neuroprosthesis}, volume={26}, url={https://doi.org/10.1109/TNSRE.2017.2756023}, DOI={10.1109/TNSRE.2017.2756023}, abstractNote={A hybrid neuroprosthesis that combines human muscle power, elicited through functional electrical stimulation (FES), with a powered orthosis may be advantageous over a sole FES or a powered exoskeleton-based rehabilitation system. The hybrid system can conceivably overcome torque reduction due to FES-induced muscle fatigue by complementarily using torque from the powered exoskeleton. The second advantage of the hybrid system is that the use of human muscle power can supplement the powered exoskeleton’s power (motor torque) requirements; thus, potentially reducing the size and weight of a walking restoration system. To realize these advantages, however, it is unknown how to concurrently optimize desired control performance and allocation of control inputs between FES and electric motor. In this paper, a model predictive control-based dynamic control allocation (DCA) is used to allocate control between FES and the electric motor that simultaneously maintain a desired knee angle. The experimental results, depicting the performance of the DCA method while the muscle fatigues, are presented for an able-bodied participant and a participant with spinal cord injury. The experimental results showed that the motor torque recruited by the hybrid system was less than that recruited by the motor-only system, the algorithm can be easily used to allocate more control input to the electric motor as the muscle fatigues, and the muscle fatigue induced by the hybrid system was found to be less than the fatigue induced by sole FES. These results validate the aforementioned advantages of the hybrid system; thus implying the hybrid technology’s potential use in walking rehabilitation.}, number={1}, journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Kirsch, Nicholas A. and Bao, Xuefeng and Alibeji, Naji A. and Dicianno, Brad E. and Sharma, Nitin}, year={2018}, month={Jan}, pages={224–232} }
 @article{alibeji_kirsch_dicianno_sharma_2017, title={A Modified Dynamic Surface Controller for Delayed Neuromuscular Electrical Stimulation}, volume={22}, url={https://doi.org/10.1109/TMECH.2017.2704915}, DOI={10.1109/TMECH.2017.2704915}, abstractNote={A widely accepted model of muscle force generation during neuromuscular electrical stimulation (NMES) is a second-order nonlinear musculoskeletal dynamics cascaded to a delayed first-order muscle activation dynamics. However, most nonlinear NMES control methods have either neglected the muscle activation dynamics or used ad hoc strategies to tackle the muscle activation dynamics, which may not guarantee control stability. We hypothesized that a nonlinear control design that includes muscle activation dynamics can improve the control performance. In this paper, a dynamic surface control approach was used to design a proportional-integral-derivative (PID)-based NMES controller that compensates for electromechanical delays in the activation dynamics. Because the muscle activation is unmeasurable, a model-based estimator was used to estimate the muscle activation in real time. The Lyapunov stability analysis confirmed that the newly developed controller achieves uniformly ultimately bounded tracking for the musculoskeletal system. Experiments were performed on two able-bodied subjects and one spinal cord injury subject using a modified leg extension machine. These experiments illustrate the performance of the new controller and compare it with a previous PID-based controller with delay compensation controller that did not consider muscle activation dynamics in the control design. These experiments support our hypothesis that a control design that includes muscle activation improves the NMES control performance.}, number={4}, journal={IEEE/ASME Transactions on Mechatronics}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Alibeji, Naji and Kirsch, Nicholas and Dicianno, Brad E. and Sharma, Nitin}, year={2017}, month={Aug}, pages={1755–1764} }
 @article{sharma_kirsch_alibeji_dixon_2017, title={A Non-Linear Control Method to Compensate for Muscle Fatigue during Neuromuscular Electrical Stimulation}, volume={4}, ISSN={2296-9144}, url={http://dx.doi.org/10.3389/frobt.2017.00068}, DOI={10.3389/frobt.2017.00068}, abstractNote={Neuromuscular electrical stimulation (NMES) is a promising technique to artificially activate muscles as a means to potentially restore the capability to perform functional tasks in persons with neurological disorders. A pervasive problem with NMES is that overstimulation of the muscle (among other factors) leads to rapid muscle fatigue, which limits the use of clinical and commercial NMES systems. The objective of this article is to develop an NMES controller that incorporates the effects of muscle fatigue during NMES-induced non-isometric contraction of the human quadriceps femoris muscle. Our previous work that used the RISE class of non-linear controllers cannot accommodate fatigue and muscle activation dynamics. A totally new control design approach and associated stability proof is required to derive a new class of NMES control design that accounts for muscle fatigue dynamics and a first-order activation dynamics, in addition to the second-order musculoskeletal dynamics. Motivated from a control method for robotic systems in a strict-feedback form, a backstepping based-non-linear NMES controller was designed to accommodate for the additional muscle activation dynamics. Further, experimentally identified estimates of the fatigue and activation dynamics were incorporated in the control design. The developed controller uses a neural network-based estimate of the musculoskeletal dynamics and error due to fatigue estimation. A globally uniformly ultimately bounded stability is proven the new controller that accounts for an uncertain non-linear muscle model and bounded non-linear disturbances (e.g., spasticity and changing load dynamics). The developed controller was validated through experiments on the left and right legs of 3 able-bodied subjects and was compared with a proportional-derivative (PD) controller and a PD augmented with a neural network. The statistical analysis showed improved control performance compared with the PD controller.}, number={DEC}, journal={Frontiers in Robotics and AI}, publisher={Frontiers Media SA}, author={Sharma, Nitin and Kirsch, Nicholas Andrew and Alibeji, Naji A. and Dixon, Warren E.}, year={2017}, month={Dec} }
 @article{allen_zhong_kirsch_dani_clark_sharma_2017, title={A Nonlinear Dynamics-Based Estimator for Functional Electrical Stimulation: Preliminary Results From Lower-Leg Extension Experiments}, volume={25}, ISSN={1534-4320 1558-0210}, url={http://dx.doi.org/10.1109/tnsre.2017.2748420}, DOI={10.1109/TNSRE.2017.2748420}, abstractNote={Miniature inertial measurement units (IMUs) are wearable sensors that measure limb segment or joint angles during dynamic movements. However, IMUs are generally prone to drift, external magnetic interference, and measurement noise. This paper presents a new class of nonlinear state estimation technique called state-dependent coefficient (SDC) estimation to accurately predict joint angles from IMU measurements. The SDC estimation method uses limb dynamics, instead of limb kinematics, to estimate the limb state. Importantly, the nonlinear limb dynamic model is formulated into state-dependent matrices that facilitate the estimator design without performing a Jacobian linearization. The estimation method is experimentally demonstrated to predict knee joint angle measurements during functional electrical stimulation of the quadriceps muscle. The nonlinear knee musculoskeletal model was identified through a series of experiments. The SDC estimator was then compared with an extended kalman filter (EKF), which uses a Jacobian linearization and a rotation matrix method, which uses a kinematic model instead of the dynamic model. Each estimator’s performance was evaluated against the true value of the joint angle, which was measured through a rotary encoder. The experimental results showed that the SDC estimator, the rotation matrix method, and EKF had root mean square errors of 2.70°, 2.86°, and 4.42°, respectively. Our preliminary experimental results show the new estimator’s advantage over the EKF method but a slight advantage over the rotation matrix method. However, the information from the dynamic model allows the SDC method to use only one IMU to measure the knee angle compared with the rotation matrix method that uses two IMUs to estimate the angle.}, number={12}, journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Allen, Marcus and Zhong, Qiang and Kirsch, Nicholas and Dani, Ashwin and Clark, William W. and Sharma, Nitin}, year={2017}, month={Dec}, pages={2365–2374} }
 @article{alibeji_sharma_2017, title={A PID-Type Robust Input Delay Compensation Method for Uncertain Euler–Lagrange Systems}, volume={25}, url={https://doi.org/10.1109/TCST.2016.2634503}, DOI={10.1109/TCST.2016.2634503}, abstractNote={Robust delay compensation techniques for uncertain nonlinear systems with unknown input delays are, in general, lacking. The result in this brief extends a modified proportional-integral derivative (PID)-type controller that contains a distributed delay term to Euler–Lagrange systems with an unknown constant input delay. Additive disturbances and uncertainties in the nonlinear system were considered in the control development and stability analysis. The stability analysis also hinges upon Lyapunov–Krasovskii functionals that were designed to prove semiglobal uniformly ultimately bounded tracking. Experiments on a 3-degree of freedom robot were performed to depict the performance of the new controller.}, number={6}, journal={IEEE Transactions on Control Systems Technology}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Alibeji, Naji and Sharma, Nitin}, year={2017}, month={Nov}, pages={2235–2242} }
 @inproceedings{bao_sheng_sharma_2017, place={Vancouver}, title={A Tube-based Model Predictive Control Method for Sharing Control Inputs in a Hybrid Neuroprosthesis}, author={Bao, X. and Sheng, Z. and Sharma, N.}, year={2017}, month={Oct} }
 @inproceedings{bao_sun_sharma_2017, title={A recurrent neural network based MPC for a hybrid neuroprosthesis system}, volume={2018-January}, ISBN={9781509028733}, url={http://dx.doi.org/10.1109/cdc.2017.8264356}, DOI={10.1109/CDC.2017.8264356}, abstractNote={Control input sequence in a hybrid neuroprosthesis that combines functional electrical stimulation (FES) and an electric motor can be optimized by a model based optimization method, like model predictive control (MPC). However, because the human muscle model is highly nonlinear, time-varying, and contains unmeasurable state variables, it is often difficult to identify the model. Therefore, a three-layer recurrence neural network (RNN) is developed in this paper, in which backpropagation through time (BPTT) is used as training technique and the internal states are used to represent the unmeasurable states. This structure shows the potential to approximate the model of the hybrid neuroprosthesis system. After the NN model is obtained, an adaptive model predictive control is used to simulate regulation and tracking tasks to test the performance of the NN training and the MPC method.}, booktitle={2017 IEEE 56th Annual Conference on Decision and Control (CDC)}, publisher={IEEE}, author={Bao, Xuefeng and Sun, Ziyue and Sharma, Nitin}, year={2017}, month={Dec}, pages={4715–4720} }
 @article{alibeji_kirsch_sharma_2017, title={An adaptive low-dimensional control to compensate for actuator redundancy and FES-induced muscle fatigue in a hybrid neuroprosthesis}, volume={59}, ISSN={0967-0661}, url={http://dx.doi.org/10.1016/j.conengprac.2016.07.015}, DOI={10.1016/j.conengprac.2016.07.015}, abstractNote={To restore walking and standing function in persons with paraplegia, a hybrid walking neuroprosthesis that combines a powered exoskeleton and functional electrical stimulation (FES) can be more advantageous than sole FES or powered exoskeleton technologies. However, the hybrid actuation structure introduces certain control challenges: actuator redundancy, cascaded muscle activation dynamics, FES-induced muscle fatigue, and unmeasurable states. In this paper, a human motor control inspired control scheme is combined with a dynamic surface control method to overcome these challenges. The new controller has an adaptive muscle synergy-based feedforward component which requires a fewer number of control signals to actuate multiple effectors in a hybrid neuroprosthesis. In addition, the feedforward component has an inverse fatigue signal to counteract the effects of the muscle fatigue. A dynamic surface control (DSC) method is used to deal with the cascaded actuation dynamics without the need for acceleration signals. The DSC structure was modified with a delay compensation term to deal with the electromechanical delays due to FES. A model based estimator is used to estimate the unmeasurable fatigue and actuator activation signals. The development of the controller and a Lyapunov stability analysis, which yielded semi-global uniformly ultimately boundedness, are presented in the paper. Computer simulations were performed to test the new controller on a 2 degrees of freedom fixed hip model after which preliminary experiments were conducted on one able-bodied male subject in the fixed hip configuration.}, journal={Control Engineering Practice}, publisher={Elsevier BV}, author={Alibeji, Naji and Kirsch, Nicholas and Sharma, Nitin}, year={2017}, month={Feb}, pages={204–219} }
 @article{alibeji_dicianno_sharma_2017, title={Bilateral control of functional electrical stimulation and robotics-based telerehabilitation}, volume={1}, url={https://doi.org/10.1007/s41315-016-0003-5}, DOI={10.1007/s41315-016-0003-5}, abstractNote={Currently, a telerehabilitation system includes a therapist and a patient where the therapist interacts with the patient, typically via a verbal and visual communication, for assessment and supervision of rehabilitation interventions. This mechanism often fails to provide physical assistance, which is a modus operandi during physical therapy or occupational therapy. Incorporating an actuation modality such as functional electrical stimulation (FES) or a robot at the patient’s end that can be controlled by a therapist remotely to provide therapy or to assess and measure rehabilitation outcomes can significantly transform current telerehabilitation technology. In this paper, a position-synchronization controller is derived for FES-based telerehabilitation to provide physical assistance that can be controlled remotely. The newly derived controller synchronizes an FES-driven human limb with a remote physical therapist’s robotic manipulator despite constant bilateral communication delays. The control design overcomes a major stability analysis challenge: the unknown and unstructured nonlinearities in the FES-driven musculoskeletal dynamics. To address this challenge, the nonlinear muscle model was estimated through two neural network functions that approximated unstructured nonlinearities and an adaptive control law for structured nonlinearities with online update laws. A Lyapunov-based stability analysis was used to prove the globally uniformly ultimately bounded tracking performance. The performance of the state synchronization controller was validated through experiments on an able-bodied subject. Specifically, we demonstrated bilateral control of FES-elicited leg extension and a human-operated robotic manipulator. The controller was shown to effectively synchronize the system despite unknown and different delays in the forward and backward channels.}, number={1}, journal={International Journal of Intelligent Robotics and Applications}, publisher={Springer Science and Business Media LLC}, author={Alibeji, Naji and Dicianno, Brad E. and Sharma, Nitin}, year={2017}, month={Feb}, pages={6–18} }
 @inbook{kirsch_alibeji_redfern_sharma_2017, place={Cham, Switzerland}, series={Biosystems & Biorobotics}, title={Dynamic Optimization of a Hybrid Gait Neuroprosthesis to Improve Efficiency and Walking Duration: A Simulation Study}, volume={15}, ISBN={9783319466682 9783319466699}, ISSN={2195-3562 2195-3570}, url={http://dx.doi.org/10.1007/978-3-319-46669-9_113}, DOI={10.1007/978-3-319-46669-9_113}, abstractNote={The walking duration of gait restoration systems that use functional electrical stimulation (FES) is severely limited by the rapid onset of muscle fatigue. Alternatively, fully actuated orthoses can also be employed to restore walking in paraplegia. However, due to the high power consumption of electric motors the walking duration of such devices are limited by the charge of the batteries. This paper proposes that a hybrid system, which uses FES and an actuated orthosis, is capable of achieving greater walking durations than an FES only system and more energetically efficient than a lower-limb exoskeleton. This is illustrated through results of optimizations of a musculoskeletal gait model for three actuation cases: FES only, electric motors only, and a hybrid system. The presented results illustrate that a hybrid system may be capable of greater walking durations than FES-based systems while using half the energy of a lower-limb exoskeleton.}, booktitle={Converging Clinical and Engineering Research on Neurorehabilitation II}, publisher={Springer International Publishing}, author={Kirsch, Nicholas A. and Alibeji, Naji A. and Redfern, Mark and Sharma, Nitin}, editor={Ibáñez, J. and González-Vargas, J. and Azorín, J. and Akay, M. and Pons, J.Editors}, year={2017}, pages={687–691}, collection={Biosystems & Biorobotics} }
 @inproceedings{dodson_alibeji_sharma_2017, title={Experimental demonstration of a delay compensating controller in a hybrid walking neuroprosthesis}, ISBN={9781509046034}, url={http://dx.doi.org/10.1109/ner.2017.8008390}, DOI={10.1109/ner.2017.8008390}, abstractNote={A hybrid neuroprosthesis is a device that uses a combination of electric motors and functional electrical stimulation (FES) to provide gait assistance. Its closed-loop control performance can be potentially affected by the presence of electromechanical delay (EMD) during FES. In this paper, a tracking control scheme for a hybrid walking neuroprosthesis that combines electric motor actuation at the hip and FES actuation at the knee is presented. The knee joint controller uses a delay compensation technique to compensate for EMD during FES. This neuroprosthesis controller is combined within a finite state machine that also features gait detection, wherein force sensors in the foot can detect gait phases and create a fully automated and functional assisted gait cycle. Experiments were performed on an able bodied subject to demonstrate the efficacy of the tracking control scheme. Results from the experiments show a maximum error at the hip of less than 1 degree and a maximum error at the knee of 13.66 degrees. The maximum error at the knee is attributed to overshoot caused by the unidirectional actuation of the FES.}, booktitle={2017 8th International IEEE/EMBS Conference on Neural Engineering (NER)}, publisher={IEEE}, author={Dodson, Albert and Alibeji, Naji and Sharma, Nitin}, year={2017}, month={May}, pages={465–468} }
 @inproceedings{sharma_2017, title={Human Motor Control Inspired Controller to Compensate for Actuator Redundancy in a Hybrid Neuroprosthesis}, booktitle={Workshop on Assistance and Service Robotics in a Human Environment}, author={Sharma, N.}, year={2017}, month={Sep} }
 @inbook{sharma_kirsch_2017, place={London}, title={Modeling and Dynamic Optimization of a Hybrid Neuroprosthesis for Gait Restoration}, ISBN={9780128031377}, url={http://dx.doi.org/10.1016/b978-0-12-803137-7.00008-2}, DOI={10.1016/b978-0-12-803137-7.00008-2}, abstractNote={A hybrid neuroprosthesis that combines functional electrical stimulation (FES) with an orthosis can be used to restore lower limb function in persons with paraplegia. This artificial intervention can substantially improve the walking duration vis-à-vis a sole FES walking system. However, it is unknown how to achieve optimal limb trajectories and their corresponding optimal control inputs for their use in the hybrid walking system. Optimization of limb angle trajectories and control inputs can minimize muscle fatigue due to FES and the metabolic fatigue in arms, which is caused by a user’s supported weight on a walker. Thus, reduction in total fatigue, due to optimization, can further enhance the benefits of the hybrid neuroprosthesis. We show that dynamic optimization can be used to compute stimulation/torque profiles and their corresponding joint angle trajectories which minimize electrical stimulation and walker push or pull forces. Importantly, the computation of these optimal stimulation or torque profiles did not require a predefined or a nominal gait trajectory (ie, a tracking control problem was not solved). Rather the trajectories were computed based only on predefined endpoints. Different optimal actuation strategies for FES and orthosis aided gait under various scenarios (eg, use of a powered or an unpowered orthosis combined with stimulation of all or a few selected lower-limb muscles) were calculated. The qualitative comparison of these results depict the advantages and disadvantages of each actuation strategy.}, booktitle={Human Modelling for Bio-Inspired Robotics}, publisher={Academic Press/Elsevier}, author={Sharma, N. and Kirsch, N.}, editor={Ueda, J. and Kurita, Y.Editors}, year={2017}, pages={139–159} }
 @article{kirsch_alibeji_sharma_2017, title={Nonlinear model predictive control of functional electrical stimulation}, volume={58}, ISSN={0967-0661}, url={http://dx.doi.org/10.1016/j.conengprac.2016.03.005}, DOI={10.1016/j.conengprac.2016.03.005}, abstractNote={Minimizing the amount of electrical stimulation can potentially mitigate the adverse effects of muscle fatigue during functional electrical stimulation (FES) induced limb movements. A gradient projection-based model predictive controller is presented for optimal control of a knee extension elicited via FES. A control Lyapunov function was used as a terminal cost to ensure stability of the model predictive control. The controller validation results show that the algorithm can be implemented in real-time with a steady-state RMS error of less than 2°. The experiments also show that the controller follows step changes in desired angles and is robust to external disturbances.}, journal={Control Engineering Practice}, publisher={Elsevier BV}, author={Kirsch, Nicholas and Alibeji, Naji and Sharma, Nitin}, year={2017}, month={Jan}, pages={319–331} }
 @inbook{alibeji_kirsch_sharma_2017, place={Cham, Switzerland}, series={Biosystems & Biorobotics}, title={Preliminary Experiments of an Adaptive Low-Dimensional Control for a Hybrid Neuroprosthesis}, volume={15}, ISBN={9783319466682 9783319466699}, ISSN={2195-3562 2195-3570}, url={http://dx.doi.org/10.1007/978-3-319-46669-9_114}, DOI={10.1007/978-3-319-46669-9_114}, abstractNote={Hybrid neuroprostheses that use both electric motor drives and functional electrical stimulation for the restoration of walking in persons with paraplegia have a promising potential. However, the hybrid actuation structure introduces effector redundancy, making the system complex and difficult to control. In this paper, preliminary experimental results of a recently developed low-dimensional controller, which is inspired from the muscle synergy principle, are presented. The experiments were performed on an able-bodied subject in a configuration where only one leg is actuated in a cycling manner while the contralateral leg was fixed.}, booktitle={Converging Clinical and Engineering Research on Neurorehabilitation II}, publisher={Springer International Publishing}, author={Alibeji, Naji A. and Kirsch, Nicholas A. and Sharma, Nitin}, editor={Ibáñez, J. and González-Vargas, J. and Azorín, J. and Akay, M. and Pons, J.Editors}, year={2017}, pages={693–697}, collection={Biosystems & Biorobotics} }
 @inproceedings{sharma_2017, title={Shared control of functional electrical stimulation and an electric motor in a hybrid neuroprosthesis}, author={Sharma, N.}, year={2017}, month={May} }
 @misc{sharma_2017, title={Shared control of functional electrical stimulation and an electric motor in a hybrid neuroprosthesis}, author={Sharma, S.}, year={2017}, month={Mar} }
 @misc{sharma_2016, title={Control methods for shared use of an electrically-stimulated human muscle and a robot in a hybrid neuroprosthetic}, author={Sharma, N.}, year={2016}, month={Jun} }
 @inproceedings{bao_kirsch_sharma_2016, title={Dynamic control allocation of a feedback linearized hybrid neuroprosthetic system}, volume={2016-July}, ISBN={9781467386821}, url={http://dx.doi.org/10.1109/acc.2016.7525534}, DOI={10.1109/acc.2016.7525534}, abstractNote={Functional electrical stimulation (FES) can be used to restore motor function to individuals with motion impairments; however, the duration of FES usage is limited by the rapid onset of muscle fatigue. A motor-assist can be used to compensate for the muscle fatigue by sharing work load of FES. However, it is unknown how to optimally allocate control effort to the motor-assist and FES as the muscle fatigues. Further, computing an optimal control solution is challenging given the nonlinear dynamics of the human leg system. This paper uses model predictive control (MPC) to solve an optimal control trajectory for the feedback linearized musculoskeletal system, which simplifies the optimal control problem; therefore, may reduce the computational load for MPC. The feedback linearization controller was developed for the nonlinear musculoskeletal model with fatigue dynamics where FES and an electric motor torque are the inputs. Then MPC was used on the linearized musculoskeletal system to allocate control to FES and an electric motor for regulation. Simulations on a musculoskeletal model of knee extension are presented in the paper.}, booktitle={2016 American Control Conference (ACC)}, publisher={IEEE}, author={Bao, Xuefeng and Kirsch, Nicholas and Sharma, Nitin}, year={2016}, month={Jul}, pages={3976–3981} }
 @inproceedings{qiu_alibeji_sharma_2016, place={Hoboken, NJ}, title={Robust compensation of electromechanical delay during neuromuscular electrical stimulation of antagonistic muscles}, volume={2016-July}, ISBN={9781467386821}, url={http://dx.doi.org/10.1109/acc.2016.7526124}, DOI={10.1109/acc.2016.7526124}, abstractNote={Neuromuscular electrical stimulation (NMES) to extend as well as flex a limb joint requires stimulation of an antagonistic muscle pair. This is due to the fact that muscles are unidirectional actuators. The control challenge is to allocate control inputs to antagonist muscles based on the system output, usually a limb angle error. Further, NMES input to each muscle is delayed by an electromechanical delay (EMD), which arises due to the time lag between the electrical excitation and the force development in a muscle. EMD is known to cause instability or performance loss during closed-loop control of NMES. In this paper, a robust delay compensation controller for EMDs in antagonistic muscles is presented. A Lyapunov stability analysis yields uniformly ultimately bounded tracking for a human limb joint actuated by antagonistic muscles. The simulation results indicate that the controller is robust and effective in switching between antagonistic muscles and compensating EMDs during a simulated NMES task.}, booktitle={2016 American Control Conference (ACC)}, publisher={IEEE}, author={Qiu, Tianyi and Alibeji, Naji and Sharma, Nitin}, year={2016}, month={Jul}, pages={4871–4876} }
 @misc{sharma_2016, title={Shared control of functional electrical stimulation and an electric motor in a hybrid neuroprosthesis}, author={Sharma, N.}, year={2016}, month={Mar} }
 @misc{sharma_2016, title={Shared control of functional electrical stimulation and an electric motor in a hybrid neuroprosthesis}, author={Sharma, N.}, year={2016}, month={Nov} }
 @inproceedings{kirsch_alibeji_dicianno_sharma_2016, place={Hoboken, NJ}, title={Switching control of functional electrical stimulation and motor assist for muscle fatigue compensation}, volume={2016-July}, ISBN={9781467386821}, url={http://dx.doi.org/10.1109/acc.2016.7526123}, DOI={10.1109/acc.2016.7526123}, abstractNote={The torque generation capability of muscles often reduces during a functional electrical stimulation (FES) session due to the rapid onset of muscle fatigue. Hybrid rehabilitation systems that use FES and electric motor assist may overcome this issue. The primary control challenge in such a system is how to allocate control inputs between electric motor and FES during muscle fatigue and muscle recovery. One strategy is to switch between FES and the electric motor by using an estimate of the muscle fatigue. This would allow the system to switch from using FES to using the electric motor when the muscle torque output has significantly decreased, then switch back to FES once the muscles have sufficiently recovered. This paper uses a second order sliding mode controller cascaded with a feedback linearization controller for a switched, FES and electric motor, system. The second order sliding mode is achieved through the use of a variable-gain super-twisting algorithm. A Lyapunov stability analysis was used to prove asymptotic stability of the switched control system. Simulations of the developed controller on a hybrid knee extension model illustrate that prolonged knee movements can be elicited through the switched system.}, booktitle={2016 American Control Conference (ACC)}, publisher={IEEE}, author={Kirsch, Nicholas and Alibeji, Naji and Dicianno, Brad E. and Sharma, Nitin}, year={2016}, month={Jul}, pages={4865–4870} }
 @article{alibeji_kirsch_sharma_2015, title={A Muscle Synergy-Inspired Adaptive Control Scheme for a Hybrid Walking Neuroprosthesis}, volume={3}, ISSN={2296-4185}, url={http://dx.doi.org/10.3389/fbioe.2015.00203}, DOI={10.3389/fbioe.2015.00203}, abstractNote={A hybrid neuroprosthesis that uses an electric motor-based wearable exoskeleton and functional electrical stimulation (FES) has a promising potential to restore walking in persons with paraplegia. A hybrid actuation structure introduces effector redundancy, making its automatic control a challenging task because multiple muscles and additional electric motor need to be coordinated. Inspired by the muscle synergy principle, we designed a low dimensional controller to control multiple effectors: FES of multiple muscles and electric motors. The resulting control system may be less complex and easier to control. To obtain the muscle synergy-inspired low dimensional control, a subject-specific gait model was optimized to compute optimal control signals for the multiple effectors. The optimal control signals were then dimensionally reduced by using principal component analysis to extract synergies. Then, an adaptive feedforward controller with an update law for the synergy activation was designed. In addition, feedback control was used to provide stability and robustness to the control design. The adaptive-feedforward and feedback control structure makes the low dimensional controller more robust to disturbances and variations in the model parameters and may help to compensate for other time-varying phenomena (e.g., muscle fatigue). This is proven by using a Lyapunov stability analysis, which yielded semi-global uniformly ultimately bounded tracking. Computer simulations were performed to test the new controller on a 4-degree of freedom gait model.}, journal={Frontiers in Bioengineering and Biotechnology}, publisher={Frontiers Media SA}, author={Alibeji, Naji A. and Kirsch, Nicholas Andrew and Sharma, Nitin}, year={2015}, month={Dec}, pages={203} }
 @article{alibeji_kirsch_sharma_2015, title={An Adaptive Low-Dimensional Control for a Hybrid Neuroprosthesis}, volume={48}, ISSN={2405-8963}, url={http://dx.doi.org/10.1016/j.ifacol.2015.10.156}, DOI={10.1016/j.ifacol.2015.10.156}, abstractNote={Hybrid neuroprostheses that use both electric motor drives and functional electrical stimulation for the restoration of walking in persons with paraplegia have a promising potential. However, the hybrid actuation structure introduces effector redundancy, making the system complex and difficult to control. In this paper we design a low-dimensional controller inspired from the muscle synergy principle. The new controller requires few control signals to actuate multiple effectors in a hybrid neuroprostheses. The development of the controller and a Lyapunov stability analysis, which yielded semi-global uniformly ultimately boundedness is presented in this paper. Computer simulations were performed to test the new controller on a 2 degree of freedom fixed hip model.}, number={20}, journal={IFAC-PapersOnLine}, publisher={Elsevier BV}, author={Alibeji, Naji A. and Kirsch, Nicholas A. and Sharma, Nitin}, year={2015}, pages={303–308} }
 @misc{sharma_2015, title={Closed-loop Control Methods for a Hybrid Neuroprosthesis}, author={Sharma, N.}, year={2015}, month={Jun} }
 @misc{sharma_2015, title={Closed-loop Control Methods for a Hybrid Neuroprosthesis}, author={Sharma, N.}, year={2015}, month={Oct} }
 @inproceedings{alibeji_kirsch_sharma_2015, title={Dynamic surface control of neuromuscular electrical stimulation of a musculoskeletal system with activation dynamics and an input delay}, volume={2015-July}, ISBN={9781479986842}, url={http://dx.doi.org/10.1109/acc.2015.7170806}, DOI={10.1109/acc.2015.7170806}, abstractNote={Neuromuscular electrical stimulation (NMES) is the application of an external electrical potential across a neuromuscular effector to generate desired limb movements. Some of the challenges faced during closed-loop control of NMES include: an electromechanical delay (EMD) in the neuromuscular activation dynamics and uncertain nonlinear musculoskeletal dynamics. In this paper, a dynamic surface control (DSC) approach was used to design an NMES controller that compensates for EMD in the activation dynamics. EMD was modeled as a known constant delay embedded in the control input to the first-order muscle activation dynamics that is cascaded to the second-order uncertain musculoskeletal system. The DSC approach was employed to avoid the “explosion of terms” associated with an integrator backstepping approach. The Lyapunov stability analysis confirmed that the DSC approach achieves semi-global uniformly ultimately bounded (SGUUB) tracking for the delayed musculoskeletal system. Simulations were performed on a 1-degree of freedom knee extension dynamics to illustrate the performance of the developed controller during a trajectory tracking task.}, booktitle={2015 American Control Conference (ACC)}, publisher={IEEE}, author={Alibeji, Naji and Kirsch, Nicholas and Sharma, Nitin}, year={2015}, month={Jul}, pages={631–636} }
 @inproceedings{ravichandar_dani_khadijah-hajdu_kirsch_zhong_sharma_2015, title={Expectation Maximization Method to Identify an Electrically Stimulated Musculoskeletal Model}, volume={2}, ISBN={9780791857250}, url={http://dx.doi.org/10.1115/dscc2015-9956}, DOI={10.1115/dscc2015-9956}, abstractNote={A system identification algorithm for a musculoskeletal system using an approximate expectation maximization (E-M) is presented. Effective control design for neuroprosthesis applications necessitates a well defined muscle model. A dynamic model of the lower leg with a fixed ankle is considered. The unknown parameters of the model are estimated using an approximate E-M algorithm based on knee angle measurements collected from an able-bodied subject during stimulated knee extension. The parameters estimated from the data are compared to reference values obtained by conducting experiments that separate the parameters in the dynamics from one another. The presented results demonstrate the capability of the proposed algorithm to identify the parameters of the dynamic model from knee angle measurements.Copyright © 2015 by ASME}, number={DSCC2015-9956DSCC2015-9956}, booktitle={Volume 2: Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications}, publisher={American Society of Mechanical Engineers}, author={Ravichandar, Harish and Dani, Ashwin and Khadijah-Hajdu, Jacquelyn and Kirsch, Nicholas and Zhong, Qiang and Sharma, Nitin}, year={2015}, month={Oct} }
 @article{alibeji_kirsch_farrokhi_sharma_2015, title={Further Results on Predictor-Based Control of Neuromuscular Electrical Stimulation}, volume={23}, ISSN={1534-4320 1558-0210}, url={http://dx.doi.org/10.1109/tnsre.2015.2418735}, DOI={10.1109/tnsre.2015.2418735}, abstractNote={Electromechanical delay (EMD) and uncertain nonlinear muscle dynamics can cause destabilizing effects and performance loss during closed-loop control of neuromuscular electrical stimulation (NMES). Linear control methods for NMES often perform poorly due to these technical challenges. A new predictor-based closed-loop controller called proportional integral derivative controller with delay compensation (PID-DC) is presented in this paper. The PID-DC controller was designed to compensate for EMDs during NMES. Further, the robust controller can be implemented despite uncertainties or in the absence of model knowledge of the nonlinear musculoskeletal dynamics. Lyapunov stability analysis was used to synthesize the new controller. The effectiveness of the new controller was validated and compared with two recently developed nonlinear NMES controllers, through a series of closed-loop control experiments on four able-bodied human subjects. Experimental results depict statistically significant improved performance with PID-DC. The new controller is shown to be robust to variations in an estimated EMD value.}, number={6}, journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Alibeji, Naji and Kirsch, Nicholas and Farrokhi, Shawn and Sharma, Nitin}, year={2015}, month={Nov}, pages={1095–1105} }
 @inproceedings{kirsch_alibeji_sharma_2015, title={Nonlinear Model Predictive Control of Functional Electrical Stimulation}, volume={2}, ISBN={9780791857250}, url={http://dx.doi.org/10.1115/dscc2015-9762}, DOI={10.1115/dscc2015-9762}, abstractNote={One of the major limitations of functional electrical stimulation (FES) is the rapid onset of muscle fatigue. Minimizing stimulation is the key to decreasing the adverse effects of muscle fatigue caused by FES. Optimal control can be used to compute the minimum amount of stimulation necessary to produce a desired motion. In this paper, a gradient projection-based model predictive controller is used for an approximate optimal control of a knee extension neuroprosthesis. A control Lyapunov function is used as a terminal cost to ensure stability of the model predictive control.Copyright © 2015 by ASME}, booktitle={Volume 2: Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications}, publisher={American Society of Mechanical Engineers}, author={Kirsch, Nicholas A. and Alibeji, Naji A. and Sharma, Nitin}, year={2015}, month={Oct} }
 @article{doll_kirsch_sharma_2015, title={Optimization of a Stimulation Train based on a Predictive Model of Muscle Force and Fatigue}, volume={48}, ISSN={2405-8963}, url={http://dx.doi.org/10.1016/j.ifacol.2015.10.162}, DOI={10.1016/j.ifacol.2015.10.162}, abstractNote={Optimizing stimulation frequency based on a mathematical model that can predict skeletal muscle force and fatigue may improve the effectiveness of functional electrical stimulation systems. Potentially, optimal stimulation patterns can maximize muscle force production while also delaying the onset of muscle fatigue. In this paper, dynamic optimization was used to generate an optimal, frequency varying pulse train that maintains a desired isometric knee extension without unnecessarily fatiguing the muscle. The optimization method employed a predictive mathematical model of muscle force and fatigue. Knee extension experiments were conducted on an able-bodied participant to identify the model parameters. To test the effectiveness of the optimized train to delay muscle fatigue, a second knee extension experiment was conducted to compare the performance of the optimized stimulation train and a 50Hz constant frequency train. The peak force and the force time integral of the optimized stimulation train were found to be higher than the 50Hz constant frequency train. These preliminary results show that optimizing stimulation patterns, based on a subject specific predictive mathematical model, may potentially delay the onset of muscle fatigue while obtaining desired force profiles.}, number={20}, journal={IFAC-PapersOnLine}, publisher={Elsevier BV}, author={Doll, Brian D. and Kirsch, Nicholas A. and Sharma, Nitin}, year={2015}, pages={338–342} }
 @inproceedings{dani_sharma_2014, title={A discrete-time nonlinear estimator for an orthosis-aided gait}, volume={1}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84929353401&partnerID=MN8TOARS}, booktitle={ASME 2014 Dynamic Systems and Control Conference, DSCC 2014}, author={Dani, A. and Sharma, N.}, year={2014} }
 @inproceedings{kirsch_alibeji_fisher_gregory_sharma_2014, title={A semi-active hybrid neuroprosthesis for restoring lower limb function in paraplegics}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84929484215&partnerID=MN8TOARS}, DOI={10.1109/EMBC.2014.6944144}, abstractNote={Through the application of functional electrical stimulation (FES) individuals with paraplegia can regain lost walking function. However, due to the rapid onset of muscle fatigue, the walking duration obtained with an FES-based neuroprosthesis is often relatively short. The rapid muscle fatigue can be compensated for by using a hybrid system that uses both FES and an active orthosis. In this paper, we demonstrate the initial testing of a semi-active hybrid walking neuroprosthesis. The semi-active hybrid orthosis (SEAHO) supports a user during the stance phase and standing while the electric motors attached to the hip section of the orthosis are used to generate hip flexion/extension. FES in SEAHO is mainly used to actuate knee flexion/extension and plantar flexion of the foot. SEAHO is controlled by a finite state machine that uses a recently developed nonlinear controller for position tracking control of the hip motors and cues from the hip angle to actuate FES and other components.}, booktitle={2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014}, author={Kirsch, N. and Alibeji, N. and Fisher, L. and Gregory, C. and Sharma, N.}, year={2014}, pages={2557–2560} }
 @inproceedings{alibeji_kirsch_sethi_sharma_2014, title={A state synchronization controller for functional electrical stimulation-based telerehabilitation}, volume={3}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84929254812&partnerID=MN8TOARS}, DOI={10.1115/DSCC2014-6139}, abstractNote={A position-synchronization controller for functional electrical stimulation (FES)-based telerehabilitation was designed. The developed controller synchronizes an FES-driven human limb with a remote physical therapist’s manipulator despite constant bilateral communication delays. The control design overcomes a major stability analysis challenge: the unknown and unstructured nonlinearities in the FES-driven musculoskeletal dynamics. To address this challenge, the nonlinear muscle model was estimated through two neural networks with online update laws. A Lyapunov-based stability analysis was used to prove the globally uniformly ultimately bounded tracking performance. The control performance of the state synchronization controller is depicted using a simulation of an FES-elicited elbow extension that is remotely controlled by a manipulator.Copyright © 2014 by ASME}, booktitle={ASME 2014 Dynamic Systems and Control Conference, DSCC 2014}, author={Alibeji, N.A. and Kirsch, N.A. and Sethi, A. and Sharma, N.}, year={2014} }
 @article{sharma_mushahwar_stein_2014, title={Dynamic optimization of FES and orthosis-based walking using simple models}, volume={22}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84892603982&partnerID=MN8TOARS}, DOI={10.1109/TNSRE.2013.2280520}, abstractNote={Computation of an analytical control solution for functional electrical stimulation (FES) and orthosis-based walking is a daunting task due to the inherent nonlinear structure of the human muscle and walking dynamics. Furthermore, since muscle fatigue and available muscle force are major limiting issues, we explored the domains of numerical optimal control methods to address these issues. We first focused on the development of simple models to represent walking movement. These models account for walking produced via a limited number of activated muscles using FES along with a novel orthosis, and an assistive device such as a walker. Using dynamic optimization, the lower limb joint angle trajectories and control inputs were computed by minimizing the cost function comprising muscle stimulation variables and forces required to push a walker. Computer simulations for optimizations were performed across a range of step lengths to find the optimal step length (minimum cost per distance). Then, the optimal steady-state initial angular velocity (for optimal step length) was computed from a range of angular velocities of the lower-limb segments. We found considerable differences between able-bodied walking trajectories and the optimal walking trajectories for FES and orthosis-based walking. Based on this computer simulation study, we recommend that instead of arbitrary selection of stimulation profiles or gait parameters, dynamic optimization can be utilized to compute gait parameters such as step length, steady state velocity, and joint angle trajectories in future clinical implementation of FES and orthosis-based walking.}, number={1}, journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering}, author={Sharma, N. and Mushahwar, V. and Stein, R.}, year={2014}, pages={114–126} }
 @inproceedings{kirsch_alibeji_sharma_2014, title={Model predictive control-based dynamic control allocation in a hybrid neuroprosthesis}, volume={3}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84929239152&partnerID=MN8TOARS}, DOI={10.1115/DSCC2014-6133}, abstractNote={To date, a functional electrical stimulation (FES)-based walking technology is incapable of enabling a paraplegic user to walk more than a few hundred meters. This is primarily due to the rapid onset of muscle fatigue, which causes limited torque generation capability of the lower-limb muscles. A hybrid walking neuroprosthesis that combines FES with an electric motor can overcome this challenge, since an electric motor can be used to compensate for any reduction in force generation due to the muscle fatigue. However, the hybrid actuation structure creates an actuator redundancy control problem; i.e., a closed-loop controller must optimally distribute torque between FES and an electric motor. Further, the control inputs to FES and an electric motor must adapt as a skeletal muscle fatigues. We consider these issues as open research control problems. In this paper, we propose that a model predictive control (MPC)-based control design can be used to optimally distribute joint torque, and can adapt as the muscle fatigue sets in. Particularly, a customized quadratic programming solver (generated using CVXGEN) was used to simulate MPC-based control of the hybrid neuroprosthesis that elicits knee extension via FES and an electric actuator.Copyright © 2014 by ASME}, booktitle={ASME 2014 Dynamic Systems and Control Conference, DSCC 2014}, author={Kirsch, N.A. and Alibeji, N.A. and Sharma, N.}, year={2014} }
 @inproceedings{sharma_dani_2014, place={Hoboken, NJ}, title={Nonlinear estimation of gait kinematics during functional electrical stimulation and orthosis-based walking}, ISBN={9781479932740 9781479932726 9781479932719}, url={http://dx.doi.org/10.1109/acc.2014.6859342}, DOI={10.1109/ACC.2014.6859342}, abstractNote={This paper presents a nonlinear estimation algorithm which utilizes a low-degree of freedom model of functional electrical stimulation (FES) and orthosis-based walking to estimate lower-limb angles. The estimated lower limb angles can be used to decide when the FES signal should be applied to the leg during the different phases of walking. To this end, we use measurements from inertial measurement units (IMUs) to estimate the lower limb segment angles. A state-dependent coefficient (SDC)-based nonlinear estimator is developed to estimate the lower limb angles. The nonlinear estimator is robust to uncertainties in the motion modeling and sensor noise/bias from the IMUs. A comparison with extended Kalman (EKF)-like filter shows improved performance of the estimator in simulation studies.}, booktitle={2014 American Control Conference}, publisher={IEEE}, author={Sharma, Nitin and Dani, Ashwin}, year={2014}, month={Jun}, pages={4778–4783} }
 @article{wang_sharma_johnson_gregory_dixon_2013, title={Adaptive inverse optimal neuromuscular electrical stimulation}, volume={43}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84890045941&partnerID=MN8TOARS}, DOI={10.1109/TSMCB.2012.2228259}, abstractNote={Neuromuscular electrical stimulation (NMES) is a prescribed treatment for various neuromuscular disorders, where an electrical stimulus is provided to elicit a muscle contraction. Barriers to the development of NMES controllers exist because the muscle response to an electrical stimulation is nonlinear and the muscle model is uncertain. Efforts in this paper focus on the development of an adaptive inverse optimal NMES controller. The controller yields desired limb trajectory tracking while simultaneously minimizing a cost functional that is positive in the error states and stimulation input. The development of this framework allows tradeoffs to be made between tracking performance and control effort by putting different penalties on error states and control input, depending on the clinical goal or functional task. The controller is examined through a Lyapunov-based analysis. Experiments on able-bodied individuals are provided to demonstrate the performance of the developed controller.}, number={6}, journal={IEEE Transactions on Cybernetics}, author={Wang, Q. and Sharma, N. and Johnson, M. and Gregory, C.M. and Dixon, W.E.}, year={2013}, pages={1710–1718} }
 @inproceedings{alibeji_kirsch_sharma_2013, title={Control of functional electrical stimulation in the presence of electromechanical and communication delays}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84897734574&partnerID=MN8TOARS}, DOI={10.1109/NER.2013.6695931}, abstractNote={In this paper, we show the feasibility of remotely controlling the elbow extension through functional electrical stimulation (FES) of the triceps muscle. Particularly, we present the experimental results obtained with the new automatic control method, designed to achieve position tracking between a user and the remote manipulator device. The major advantage of the controller is its ability to compensate for the electromechanical delay (EMD) during an FES and the communication delay (CD) due to a remote actuation. Another advantage of the developed FES controller is that only the error state and delay knowledge are required to elicit desired muscle contractions, i.e., the control implementation does not depend on model knowledge of highly nonlinear and time-varying muscle dynamics. The experimental results show its superior performance in comparison to the proportional integral derivative (PID) controller. The control performance of the PID controller and the new controller were tested for different values of a composite delay (EMD + CD).}, booktitle={International IEEE/EMBS Conference on Neural Engineering, NER}, author={Alibeji, N. and Kirsch, N. and Sharma, N.}, year={2013}, pages={299–302} }
 @inproceedings{kirsch_alibeji_sharma_2013, title={Optimized control of different actuation strategies for FES and orthosis aided gait}, volume={1}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84902488675&partnerID=MN8TOARS}, DOI={10.1115/DSCC2013-4080}, abstractNote={A combination of functional electrical stimulation (FES) and an orthosis can be used to restore lower limb function in persons with paraplegia. This artificial intervention may allow them to regain the ability to walk again, however, only for short time durations. To improve the time duration of hybrid (FES and orthosis) gait, the muscle fatigue due to FES and the fatigue in arms, caused by a user’s supported weight on a walker, needs to be minimized. In this paper, we show that dynamic optimization can be used to compute stimulation/torque profiles and their corresponding joint angle trajectories which minimize electrical stimulation and walker push or pull forces. Importantly, the computation of these optimal stimulation or torque profiles did not require a predefined or a nominal gait trajectory (i.e., a tracking control problem was not solved). Rather the trajectories were computed based only on pre-defined end-points. For optimization we utilized the recently developed three-link dynamic walking model, which includes both single and double support phases and muscle dynamics. Moreover, different optimal actuation strategies for FES and orthosis aided gait under various scenarios (e.g., use of a powered or an unpowered orthosis combined with stimulation of all or few selected lower-limb muscles) were calculated. The qualitative comparison of these results depict the advantages and disadvantages of each actuation strategy. The computed optimal FES/orthosis aided gait were also compared with able-bodied trajectories to illustrate how they differed from able-bodied walking.Copyright © 2013 by ASME}, booktitle={ASME 2013 Dynamic Systems and Control Conference, DSCC 2013}, author={Kirsch, N. and Alibeji, N.A. and Sharma, N.}, year={2013} }
 @article{fischer_dani_sharma_dixon_2013, title={Saturated control of an uncertain nonlinear system with input delay}, volume={49}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84877574752&partnerID=MN8TOARS}, DOI={10.1016/j.automatica.2013.02.013}, abstractNote={This paper examines saturated control of a general class of uncertain nonlinear systems with time-delayed actuation and additive bounded disturbances. The bound on the control is known a priori and can be adjusted by changing the feedback gains. A Lyapunov-based stability analysis utilizing Lyapunov–Krasovskii (LK) functionals is provided to prove uniformly ultimately bounded tracking despite uncertainties in the dynamics. A numerical example is presented to demonstrate the performance of the controller.}, number={6}, journal={Automatica}, author={Fischer, N. and Dani, A. and Sharma, N. and Dixon, W.E.}, year={2013}, pages={1741–1747} }
 @inproceedings{sharma_2012, title={A predictor-based compensation for electromechanical delay during neuromuscular electrical stimulation-II}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84869406024&partnerID=MN8TOARS}, booktitle={Proceedings of the American Control Conference}, author={Sharma, N.}, year={2012}, pages={5604–5609} }
 @article{sharma_gregory_johnson_dixon_2012, title={Closed-loop neural network-based NMES control for human limb tracking}, volume={20}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84859898238&partnerID=MN8TOARS}, DOI={10.1109/TCST.2011.2125792}, abstractNote={Closed-loop control of skeletal muscle is complicated by the nonlinear muscle force to length and velocity relationships and the inherent unstructured and time-varying uncertainties in available models. Some pure feedback methods have been developed with some success, but the most promising and popular control methods for neuromuscular electrical stimulation (NMES) are neural network (NN)-based methods. Efforts in this paper focus on the use of a NN feedforward controller that is augmented with a continuous robust feedback term to yield an asymptotic result (in lieu of typical uniformly ultimately bounded stability). Specifically, an NN-based controller and Lyapunov-based stability analysis are provided to enable semi-global asymptotic tracking of a desired limb time-varying trajectory (i.e., non-isometric contractions). The developed controller is applied as an amplitude modulated voltage to external electrodes attached to the distal-medial and proximal-lateral portion of the quadriceps femoris muscle group in non-impaired volunteers. The added value of incorporating a NN feedforward term is illustrated through experiments that compare the developed controller with and without the NN feedforward component.}, number={3}, journal={IEEE Transactions on Control Systems Technology}, author={Sharma, N. and Gregory, C.M. and Johnson, M. and Dixon, W.E.}, year={2012}, pages={712–725} }
 @inproceedings{sharma_stein_2012, title={Gait planning and double support phase model for functional electrical stimulation-based walking}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84870792428&partnerID=MN8TOARS}, DOI={10.1109/EMBC.2012.6346325}, abstractNote={Joint or segment angle trajectories of able-bodied persons are often recorded or mimicked as reference trajectories for walking restoration in paraplegia. In this paper, lower limb segment angle trajectories are computed from simple mathematical models developed to represent functional electrical stimulation (FES) and a novel brace based walking. The new models incorporate the double support and single support phases of walking. Dynamic optimization is utilized to design walking trajectories that minimize muscle activations and arm reaction forces generated from the walker. Compared to the voluntary walking trajectories, the new trajectories are more representative of FES-based walking as only a limited number of muscle are stimulated to compute walking trajectories.}, booktitle={Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS}, author={Sharma, N. and Stein, R.}, year={2012}, pages={1904–1907} }
 @article{sharma_stein_2012, title={Gait planning and double support phase model for functional electrical stimulation-based walking.}, volume={2012}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84880821464&partnerID=MN8TOARS}, journal={Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference}, author={Sharma, N. and Stein, R.}, year={2012}, pages={1904–1907} }
 @misc{sharma_2012, title={Lyapunov-based Control and Trajectory Planning for Restoring Human Limb Function}, author={Sharma, N.}, year={2012}, month={Feb} }
 @misc{sharma_2012, title={Lyapunov-based Control and Trajectory Planning for Restoring Human Limb Function,}, author={Sharma, N.}, year={2012}, month={Mar} }
 @misc{sharma_2012, title={Nonlinear Control and Optimization Methods for Functional Electrical Stimulation}, author={Sharma, N.}, year={2012}, month={Jan} }
 @misc{sharma_2012, title={Nonlinear Control and Optimization Methods for Functional Electrical Stimulation}, author={Sharma, N.}, year={2012}, month={Feb} }
 @misc{nonlinear control and optimization methods for restoring walking via neuroprosthetics_2012, year={2012}, month={Apr} }
 @misc{sharma_2012, title={Nonlinear Control and Optimization Methods for Restoring Walking via Neuroprosthetics}, author={Sharma, N.}, year={2012}, month={Mar} }
 @inproceedings{fischer_kamalapurkar_sharma_dixon_2012, title={RISE-based control of an uncertain nonlinear system with time-varying state delays}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84874277756&partnerID=MN8TOARS}, DOI={10.1109/CDC.2012.6427002}, abstractNote={This paper considers a continuous control design for second-order control affine nonlinear systems with time-varying state delays. A neural network is augmented with a robust integral of the sign of the error (RISE) control structure to achieve semi-global asymptotic tracking in the presence of unknown, arbitrarily large, time-varying delays, not linear-in-the-parameters uncertainty and additive bounded disturbances. By expressing unknown functions in terms of the desired trajectories and through strategic grouping of delay-free and delay-dependent terms, Lyapunov-Krasovskii functionals are utilized to cancel the delayed terms in the analysis and obtain delay-free neural network update laws.}, booktitle={Proceedings of the IEEE Conference on Decision and Control}, author={Fischer, N. and Kamalapurkar, R. and Sharma, N. and Dixon, W.E.}, year={2012}, pages={3502–3507} }
 @article{sharma_bhasin_wang_dixon_2012, title={Rise-based adaptive control of a control affine uncertain nonlinear system with unknown state delays}, volume={57}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84855366110&partnerID=MN8TOARS}, DOI={10.1109/TAC.2011.2166314}, abstractNote={A continuous robust adaptive control method is designed for a class of uncertain nonlinear systems with unknown constant time-delays in the states. Specifically, a robust adaptive control method and a delay-free gradient-based desired compensation adaptation law (DCAL) are utilized to compensate for unknown time-delays, linearly parameterizable uncertainties, and additive bounded disturbances for a general nonlinear system. Despite these disturbances, a Lyapunov Krasovskii-based analysis is used to conclude that the system output asymptotically tracks a desired time varying bounded trajectory.}, number={1}, journal={IEEE Transactions on Automatic Control}, author={Sharma, N. and Bhasin, S. and Wang, Q. and Dixon, W.E.}, year={2012}, pages={255–259} }
 @article{downey_bellman_sharma_wang_gregory_dixon_2011, title={A novel modulation strategy to increase stimulation duration in neuromuscular electrical stimulation}, volume={44}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-80052046335&partnerID=MN8TOARS}, DOI={10.1002/mus.22058}, abstractNote={Introduction: Neuromuscular electrical stimulation (NMES) has been shown to be an effective treatment for muscular dysfunction. Yet, a fundamental barrier to NMES treatments is the rapid onset of muscle fatigue. The purpose of this study is to examine the effect of feedback‐based frequency modulation on the closed‐loop performance of the quadriceps during repeated dynamic contractions. Methods: In the first experiment, subjects completed four different frequency modulation NMES protocols utilizing the same amplitude modulation control to compare the successful run times (SRTs). A second experiment was performed to determine the change in muscle response to high‐ and low‐frequency stimulation. Results: Compared with constant‐frequency stimulation, results indicate that using an error‐driven strategy to vary the stimulation frequency during amplitude modulation increases the number of successful contractions during non‐isometric conditions. Conclusion: Simultaneous frequency and amplitude modulation increases the SRT during closed‐loop NMES control. Muscle Nerve 44: 382–387, 2011}, number={3}, journal={Muscle and Nerve}, author={Downey, R.J. and Bellman, M. and Sharma, N. and Wang, Q. and Gregory, C.M. and Dixon, W.E.}, year={2011}, pages={382–387} }
 @article{bhasin_sharma_patre_dixon_2011, title={Asymptotic tracking by a reinforcement learning-based adaptive critic controller}, volume={9}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-79960468564&partnerID=MN8TOARS}, DOI={10.1007/s11768-011-0170-8}, number={3}, journal={Journal of Control Theory and Applications}, author={Bhasin, S. and Sharma, N. and Patre, P. and Dixon, W.}, year={2011}, pages={400–409} }
 @inproceedings{sharma_stein_2011, title={Optimal trajectory planning for a constrained functional electrical stimulation-based human walking}, volume={2011}, ISBN={9781457715891 9781424441211 9781424441228}, url={http://dx.doi.org/10.1109/iembs.2011.6090134}, DOI={10.1109/IEMBS.2011.6090134}, abstractNote={In contrast to the muscle recruitment during voluntary walking, only a limited number of muscles are activated during functional electrical stimulation (FES)-based walking. This implies that a trajectory designed or recorded from the normal human walking data may not be the best choice for tracking control. Another major challenge during FES-based walking is the rapid onset of muscle fatigue. Two methods to reduce fatigue during FES-based walking are employing an orthosis and minimizing muscle activations. To deal with these aforementioned challenges, this paper presents firstly a dynamic model representing FES-elicited walking constrained by an orthosis and a walker. Secondly, this paper deals with the design of optimal stimulation and force profiles (instead of gait-trajectories from able-bodied humans) that minimize muscle activations via FES and arm reaction forces from the walker. Ten walking steps are simulated to show the feasibility of the walking model and optimization algorithm.}, booktitle={2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society}, publisher={IEEE}, author={Sharma, Nitin and Stein, Richard}, year={2011}, month={Aug}, pages={603–607} }
 @article{sharma_gregory_dixon_2011, title={Predictor-based compensation for electromechanical delay during neuromuscular electrical stimulation}, volume={19}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-83455220174&partnerID=MN8TOARS}, DOI={10.1109/TNSRE.2011.2166405}, abstractNote={Electromechanical delay (EMD) is a biological artifact that arises due to a time lag between electrical excitation and tension development in a muscle. EMD is known to cause degraded performance and instability during neuromuscular electrical stimulation (NMES). Compensating for such input delay is complicated by the unknown nonlinear muscle force-length and muscle force-velocity relationships. This paper provides control development and a mathematical stability analysis of a NMES controller with a predictive term that actively accounts for EMD. The results are obtained through the development of a novel predictor-type method to address the delay in the voltage input to the muscle. Lyapunov-Krasovskii functionals are used within a Lyapunov-based stability analysis to prove semi-global uniformly ultimately bounded tracking. Experiments on able-bodied volunteers illustrate the performance and robustness of the developed controller during a leg extension trajectory following task.}, number={6}, journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering}, author={Sharma, N. and Gregory, C.M. and Dixon, W.E.}, year={2011}, pages={601–611} }
 @article{sharma_bhasin_wang_dixon_2011, title={Predictor-based control for an uncertain EulerLagrange system with input delay}, volume={47}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-80053625733&partnerID=MN8TOARS}, DOI={10.1016/j.automatica.2011.03.016}, abstractNote={Controlling a nonlinear system with actuator delay is a challenging problem because of the need to develop some form of prediction of the nonlinear dynamics. Developing a predictor-based controller for an uncertain system is especially challenging. In this paper, tracking controllers are developed for an Euler–Lagrange system with time-delayed actuation, parametric uncertainty, and additive bounded disturbances. The developed controllers represent the first input delayed controllers developed for uncertain nonlinear systems that use a predictor to compensate for the delay. The results are obtained through the development of a novel predictor-like method to address the time delay in the control input. Lyapunov–Krasovskii functionals are used within a Lyapunov-based stability analysis to prove semi-globally uniformly ultimately bounded tracking. Experimental results illustrate the performance and robustness of the developed control methods.}, number={11}, journal={Automatica}, author={Sharma, N. and Bhasin, S. and Wang, Q. and Dixon, W.E.}, year={2011}, pages={2332–2342} }
 @inproceedings{fischer_dani_sharma_dixon_2011, title={Saturated control of an uncertain Euler-Lagrange system with input delay}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-84860653508&partnerID=MN8TOARS}, DOI={10.1109/CDC.2011.6160646}, abstractNote={This paper examines saturated control of a general class of uncertain nonlinear Euler-Lagrange systems with time-delayed actuation and additive bounded disturbances. The bound on the control is known a priori and can be adjusted by changing the feedback gains. A Lyapunov-based stability analysis utilizing Lyapunov-Krasovskii functionals is provided to prove uniformly ultimately bounded tracking despite uncertainties in the dynamics.}, booktitle={Proceedings of the IEEE Conference on Decision and Control}, author={Fischer, N. and Dani, A. and Sharma, N. and Dixon, W.E.}, year={2011}, pages={7587–7592} }
 @inproceedings{wang_sharma_johnson_dixon_2010, title={Adaptive inverse optimal neuromuscular electrical stimulation}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-78649353419&partnerID=MN8TOARS}, DOI={10.1109/ISIC.2010.5612877}, abstractNote={Neuromuscular Electrical Stimulation (NMES) (also described as functional electrical stimulation (FES) in some scenarios) is a prescribed treatment for various neuromuscular disorders where an electrical stimulus is provided to elicit a muscle contraction. Barriers to the development of NMES controllers exist because the muscle response to an electrical stimulation is nonlinear and the muscle model is uncertain. Several recent adaptive control results have been developed to enable a stimulated limb to track a desired limb trajectory. Yet, feedback methods (especially adaptive and robust methods) have a potential for overstimulation that can lead to faster muscle fatigue. Efforts in this paper focus on the development of a first ever inverse optimal NMES controller that yields an optimal limb tracking result. That is, a controller is designed that achieves desired limb trajectory tracking while simultaneously minimizing a cost functional that is positive in the error states and stimulation input. The inverse optimal controller is examined through a Lyapunov-based analysis and simulations.}, booktitle={IEEE International Symposium on Intelligent Control - Proceedings}, author={Wang, Q. and Sharma, N. and Johnson, M. and Dixon, W.E.}, year={2010}, pages={1287–1292} }
 @inproceedings{wang_sharma_johnson_dixon_2010, title={Asymptotic optimal control of neuromuscular electrical stimulation}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-79953147321&partnerID=MN8TOARS}, DOI={10.1109/CDC.2010.5717462}, abstractNote={Muscle fatigue during electrical stimulation onsets early and is comparatively more substantial than during volitional contractions, hindering successful application of functional and therapeutic neuromuscular electrical stimulation (NMES). One of the avoidable causes of muscle fatigue can be attributed to the overstimulation during NMES. In this paper, a NMES controller is developed to minimize a quadratic cost functional to balance asymptotic trajectory tracking performance and control effort, potentially reducing overstimulation of the muscle. A Lyapunov-based analysis is used to prove the asymptotic convergence of closed-loop tracking error and asymptotic minimization of the given cost functional.}, booktitle={Proceedings of the IEEE Conference on Decision and Control}, author={Wang, Q. and Sharma, N. and Johnson, M. and Dixon, W.E.}, year={2010}, pages={839–844} }
 @inproceedings{sharma_dixon_2010, title={Compensating Input Delay and Muscle Fatigue during Neuromuscular Electrical Stimulation Control}, url={http://biomedsym.beckman.illinois.edu/posters/Sharma2.pdf}, author={Sharma, N. and Dixon, W.E.}, year={2010}, month={Apr} }
 @inproceedings{sharma_2010, title={Lyapunov-based Control Methods for Neuromuscular Electrical Stimulation}, author={Sharma, N.}, year={2010}, month={Sep} }
 @inproceedings{sharma_patre_gregory_dixon_2010, title={Nonlinear control of NMES: Incorporating fatigue and calcium dynamics}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-77953794841&partnerID=MN8TOARS}, DOI={10.1115/DSCC2009-2642}, abstractNote={Neuromuscular electrical stimulation (NMES) is a promising technique that has the potential to restore functional tasks in persons with movement disorders. Clinical and commercial NMES products exist for this purpose, but a pervasive problem with current technology is that overstimulation of the muscle (among other factors) leads to muscle fatigue. The objective of the current effort is to develop a NMES controller that incorporates the effects of muscle fatigue through an uncertain function of the calcium dynamics. A neural network-based estimate of the fatigue model mismatch is incorporated in a nonlinear controller through a backstepping based method to control the human quadriceps femoris muscle undergoing non-isometric contractions. The developed controller is proven to yield uniformly ultimately bounded stability for an uncertain nonlinear muscle model in the presence of bounded nonlinear disturbances (e.g., spasticity, delays, changing load dynamics).© 2009 ASME}, number={PART A}, booktitle={Proceedings of the ASME Dynamic Systems and Control Conference 2009, DSCC2009}, author={Sharma, N. and Patre, P.M. and Gregory, C.M. and Dixon, W.E.}, year={2010}, pages={705–712} }
 @inproceedings{sharma_bhasin_wang_dixon_2010, title={Predictor-based control for an uncertain Euler-Lagrange system with input delay}, ISBN={9781424474271 9781424474264 9781424474257}, url={http://dx.doi.org/10.1109/acc.2010.5531212}, DOI={10.1109/acc.2010.5531212}, abstractNote={Control of nonlinear systems with actuator delay is a challenging problem because of the need to develop some form of prediction of the nonlinear dynamics. The problem becomes more difficult for systems with uncertain dynamics. In this paper, tracking controllers are developed for an Euler-Lagrange system with time-delayed actuation, parametric uncertainty, and additive bounded disturbances. One controller is developed under the assumption that the inertia is known, and a second controller is developed when the inertia is unknown. For each case a predictor-like method is developed to address the time delay in the control input. Lyapunov-Krasovskii functionals are used within a Lyapunov-based stability analysis to prove semi-global uniformly ultimately bounded tracking.}, booktitle={Proceedings of the 2010 American Control Conference}, publisher={IEEE}, author={Sharma, N and Bhasin, S and Wang, Q and Dixon, W E}, year={2010}, month={Jun}, pages={1422–1427} }
 @inproceedings{sharma_bhasin_wang_dixon_2010, title={RISE-based adaptive control of an uncertain nonlinear system with unknown state delays}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-79953148067&partnerID=MN8TOARS}, DOI={10.1109/CDC.2010.5716973}, abstractNote={A continuous robust adaptive control method is designed for a class of uncertain nonlinear systems with unknown constant time-delays in the states. Specifically, a robust adaptive control method, a gradient-based desired compensation adaptation law (DCAL), and a Lyapunov-Kravoskii (LK) functional-based delay control term are utilized to compensate for unknown time-delays, linearly parameterizable uncertainties, and additive bounded disturbances for a general nonlinear system. Despite these disturbances, a Lyapunov-based analysis is used to conclude that the system output asymptotically tracks a desired time varying bounded trajectory.}, booktitle={Proceedings of the IEEE Conference on Decision and Control}, author={Sharma, N. and Bhasin, S. and Wang, Q. and Dixon, W.E.}, year={2010}, pages={1773–1778} }
 @inproceedings{bhasin_sharma_patre_dixon_2010, title={Robust asymptotic tracking of a class of nonlinear systems using an adaptive critic based controller}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-77957804698&partnerID=MN8TOARS}, booktitle={Proceedings of the 2010 American Control Conference, ACC 2010}, author={Bhasin, S. and Sharma, N. and Patre, P.M. and Dixon, W.E.}, year={2010}, pages={3223–3228} }
 @article{sharma_stegath_gregory_dixon_2009, title={Nonlinear neuromuscular electrical stimulation tracking control of a human limb}, volume={17}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-74549157252&partnerID=MN8TOARS}, DOI={10.1109/TNSRE.2009.2023294}, abstractNote={A high-level objective of neuromuscular electrical stimulation (NMES) is to enable a person to achieve some functional task. Towards this goal, the objective of the current effort is to develop a NMES controller to produce a knee position trajectory that will enable a human shank to track any continuous desired trajectory (or constant setpoint). A nonlinear control method is developed to control the human quadriceps femoris muscle undergoing nonisometric contractions. The developed controller does not require a muscle model and can be proven to yield asymptotic stability for a nonlinear muscle model in the presence of bounded nonlinear disturbances (e.g., spasticity, delays, fatigue). The performance of the controller is demonstrated through a series of closed-loop experiments on human subjects. The experiments illustrate the ability of the controller to enable the leg shank to track single and multiple period trajectories with different periods and ranges of motion, and also track desired step changes with changing loads.}, number={6}, journal={IEEE Transactions on Neural Systems and Rehabilitation Engineering}, author={Sharma, N. and Stegath, K. and Gregory, C.M. and Dixon, W.E.}, year={2009}, pages={576–584} }
 @article{gregory_bickel_sharma_dixon_2008, title={Comparing the force- and excursion-frequency relationships in human skeletal muscle}, volume={38}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-56749158074&partnerID=MN8TOARS}, DOI={10.1002/mus.21161}, abstractNote={We examined the influence of varying stimulation frequency on muscle output during isometric and dynamic contractions. Our findings demonstrate that the predictability of the force– and excursion–frequency relationships is extremely strong across stimulation intensities. There were no differences in the frequency which elicited 50% of peak or peak muscle output. We conclude that the impact of varying stimulation frequency is consistent between isometric and dynamic contractions. Muscle Nerve, 2008}, number={6}, journal={Muscle and Nerve}, author={Gregory, C.M. and Bickel, C.S. and Sharma, N. and Dixon, W.E.}, year={2008}, pages={1627–1629} }
 @inproceedings{sharma_gregory_johnson_dixon_2008, title={Modified neural network-based electrical stimulation for human limb tracking}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-56749154805&partnerID=MN8TOARS}, DOI={10.1109/ISIC.2008.4635968}, abstractNote={Closed-loop control of skeletal muscle is complicated by the nonlinear muscle force to length relationship and the inherent unstructured and time-varying uncertainties in available models. Some pure feedback methods have been developed with some success, but the most promising and popular control methods for neuromuscular electrical stimulation (NMES) are neural network-based methods. Neural networks provide a function approximation of the muscle model, however a function reconstruction error limits the steady-state response of typical controllers (i.e., previous controllers are only uniformly ultimately bounded). Motivated by the desire to obtain improved steady-state performance, efforts in this paper focus on the use of a neural network feedforward controller that is augmented with a continuous robust feedback term to yield an asymptotic result. Specifically, a Lyapunov-based controller and stability analysis are provided to demonstrate semi-global asymptotic tracking (i.e., non-isometric contractions) of a desired time-varying trajectory. Experimental results are provided to demonstrate the performance of the developed controller where NMES is applied through external electrodes attached to the distal-medial and proximal-lateral portion of human quadriceps femoris muscle group.}, booktitle={IEEE International Symposium on Intelligent Control - Proceedings}, author={Sharma, N. and Gregory, C.M. and Johnson, M. and Dixon, W.E.}, year={2008}, pages={1320–1325} }
 @inproceedings{stegath_sharma_gregory_dixon_2008, title={Nonlinear tracking control of a human limb via neuromuscular electrical stimulation}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-52449111364&partnerID=MN8TOARS}, DOI={10.1109/ACC.2008.4586776}, abstractNote={A nonlinear control method is developed in this paper that uses neuromuscular electrical stimulation to control the human quadriceps femoris muscle undergoing non- isometric contractions. The objective of the controller is to position the lower limb of a human along a time-varying trajectory or a desired setpoint. The developed controller does not require a muscle model and can be proven to yield asymptotic stability for a nonlinear muscle model in the presence of bounded nonlinear disturbances. Performance of the controller is illustrated in the provided experimental results.}, booktitle={Proceedings of the American Control Conference}, author={Stegath, K. and Sharma, N. and Gregory, C.M. and Dixon, W.E.}, year={2008}, pages={1941–1946} }
 @inproceedings{stegath_sharma_gregory_dixon_2007, title={An extremum seeking method for non-isometric neuromuscular electrical stimulation}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-40949153396&partnerID=MN8TOARS}, DOI={10.1109/ICSMC.2007.4413886}, abstractNote={An optimal extremum seeking approach is developed in this paper to identify frequency and voltage modulation parameters for a neuromuscular electrical stimulation control objective. The control objective is to externally apply optimally varied voltage or frequency modulation parameters to a human quadriceps muscle to generate a desired knee joint angle. Experimental results are provided to illustrate the limb positioning performance of a real-time extremum seeking routine (i.e., Brent's Method).}, booktitle={Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics}, author={Stegath, K. and Sharma, N. and Gregory, C.M. and Dixon, W.E.}, year={2007}, pages={2528–2532} }
 @inproceedings{stegath_sharma_gregory_dixon_2007, title={Experimental demonstration of rise-based NMES of human quadriceps muscle}, url={http://www.scopus.com/inward/record.url?eid=2-s2.0-50849124105&partnerID=MN8TOARS}, DOI={10.1109/LSSA.2007.4400880}, abstractNote={The nonlinear control method explored in this paper uses voltage modulation for controlling human quadriceps femoris muscle undergoing non-isometric neuromuscular electrical stimulation. The control objective is to provide computer controlled stimulation to enable the lower limb of a human to either go to a desired constant position or follow a time-varying desired trajectory. An advantage of the developed controller is that it is robust to nonlinear disturbances and does not require a muscle model. Experimental results are provided that illustrate the performance of the controller.}, booktitle={2007 IEEE/NIH Life Science Systems and Applications Workshop, LISA}, author={Stegath, K. and Sharma, N. and Gregory, C.M. and Dixon, W.E.}, year={2007}, pages={43–46} }