@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{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} }
 @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} }