@article{kennedy_vlajic_perkins_2024, title={Hybrid dynamical modeling of shape memory alloy actuators with phase kinetic equations}, ISSN={["1530-8138"]}, DOI={10.1177/1045389X241265617}, abstractNote={Shape memory alloy morphing actuators are a type of composite soft actuator with many attractive properties such as large deformation, small form factor, self-sensing ability, and physical reservoir computing potential. These actuators are composed of active shape memory alloy wires and a passive material to magnify the overall deflection. However, the dynamic modeling of these actuators is difficult due to both shape memory alloy characteristics and the nonlinearity of the passive layer. Here, a hybrid dynamical model is proposed that couples the phase kinetics and thermal modeling for the shape memory alloy with a dynamic Cosserat beam model. This hybrid model is benchmarked against experimental linear and morphing actuators resulting in a root mean squared error of 0.87 mm for the linear actuator and root mean squared error of 1.34 and 1.42 mm for the two morphing actuator configurations evaluated in this work. This model applies continuous phase kinetic equations in a comprehensive hybrid dynamical model to accurately simulate the hysteretic transition of the alloy, which is then coupled to a high deformation beam model. This work can expand the capability and design of novel morphing actuators to achieve specified dynamic characteristics for increased application in robotic fields.}, journal={JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES}, author={Kennedy, Scott and Vlajic, Nicholas and Perkins, Edmon}, year={2024}, month={Aug} }
 @article{ul shougat_kennedy_perkins_2023, title={A Self-Sensing Shape Memory Alloy Actuator Physical Reservoir Computer}, volume={7}, ISSN={["2475-1472"]}, DOI={10.1109/LSENS.2023.3270704}, abstractNote={A self-sensing shape memory alloy actuator is harnessed as a computational resource by utilizing it as a physical reservoir computer. Physical reservoir computing is a machine learning technique that takes advantage of the dynamics of a physical system for computation. Compared to recurrent neural networks, this architecture can be both fast and efficient with a cheaper training procedure. A shape memory alloy actuator is designed, fabricated, and tested for processing information. Voltage variation along the shape memory alloy wire is used as the reservoir's nodes. The physical reservoir is then used to predict the future trajectory of the actuator's end effector under various driving signals. This self-prediction method is also reconfigurable, as demonstrated by training the reservoir for one waveform but testing it for a different one. A nonlinear autoregressive moving average prediction task was also used to highlight the physical reservoir computer's abilities. Following this methodology, the soft actuator can be used for actuation and computation at the same time without altering its design.}, number={5}, journal={IEEE SENSORS LETTERS}, author={Ul Shougat, Md Raf E. and Kennedy, Scott and Perkins, Edmon}, year={2023}, month={May} }
 @article{kennedy_ul shougat_perkins_2023, title={Robust self-sensing shape memory alloy actuator using a machine learning approach}, volume={354}, ISSN={["1873-3069"]}, DOI={10.1016/j.sna.2023.114255}, abstractNote={Shape memory alloys are metal alloys that have multiple crystalline states, which can be accessed through heating and cooling. These actuators have several attractive properties, such as a high strength-to-weight ratio, robustness, and compact structure. Added to this, shape memory alloys have a self-sensing property. However, shape memory alloy wires typically have a low contraction of only ∼4%–5%. To overcome this shortcoming, shape memory alloy actuators can use a passive base layer in a morphing configuration to amplify the deformation. By including voltage probes at the base of a unimorph shape memory alloy actuator, self-sensing of the actuator’s configuration can be achieved, without encumbering the actuator with extra mass or wires. Due to the compact nature of this actuator, the voltage probes are necessarily close together, which drastically increases the errors of the estimated deformations. To correct these large errors, a machine learning approach is used to unlock this self-sensing ability for a compact shape memory alloy actuator. Of note, this approach, instead of being a black box method, builds on a self-sensing theory of shape memory alloys. This method of self-sensing with machine learning calibration is capable of predicting the location of the end of the actuator with less than 3% error, even for large deformations.}, journal={SENSORS AND ACTUATORS A-PHYSICAL}, author={Kennedy, Scott and Ul Shougat, Md Raf E. and Perkins, Edmon}, year={2023}, month={May} }
 @article{kennedy_vlajic_perkins_2022, title={Cosserat modeling for deformation configuration of shape memory alloy unimorph actuators}, volume={8}, ISSN={["1530-8138"]}, DOI={10.1177/1045389X221109256}, abstractNote={ Shape memory alloys (SMAs) can contract their length via a crystalline phase transition that is dependent upon their temperature and stress state. SMAs have been used as linear micro-actuators due to their high strength to weight ratio and compact structure. However, the relatively low linear contraction ([Formula: see text]4%–5% in length) limits their use. To remedy this, the SMA can be offset from a passive structure, which acts to magnify the deformation. The resulting amount of deformation depends upon the material properties and geometry of both the SMA and the passive structure. In this work, geometrically exact beam theory (also known as Cosserat theory) is coupled with SMA constitutive relations to model the maximum deformation configuration of these actuators. Four of these actuators of various lengths were fabricated and tested to verify the model. For the four actuators tested, the mean squared error between the experimental results and the Cosserat model were between 0.0702 mm (0.1% error) for the shortest actuator (66 mm in length) and 3.59 mm (2.7% error) for the longest actuator (135 mm in length). These results show that the closed form solution derived for this Cosserat beam model can accurately model the deformation of these active structures. }, journal={JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES}, author={Kennedy, Scott and Vlajic, Nicholas and Perkins, Edmon}, year={2022}, month={Aug} }
 @article{mollik_kennedy_ul shougat_li_fitzgerald_echols_kirk_silverberg_perkins_2022, title={Discrete element method simulator for joint dynamics: a case study using a red-tailed hawk's hallux digit}, volume={6}, ISSN={["1573-272X"]}, DOI={10.1007/s11044-022-09828-x}, journal={MULTIBODY SYSTEM DYNAMICS}, author={Mollik, Tushar and Kennedy, Scott and Ul Shougat, Md Raf E. and Li, Xiaofu and Fitzgerald, Timothy and Echols, Scott and Kirk, Nick and Silverberg, Larry and Perkins, Edmon}, year={2022}, month={Jun} }
 @article{li_kallepalli_mollik_ul shougat_kennedy_frabitore_perkins_2022, title={The pendulum adaptive frequency oscillator}, volume={179}, ISSN={["1096-1216"]}, DOI={10.1016/j.ymssp.2022.109361}, abstractNote={Adaptive oscillators are a type of nonlinear oscillator that are capable of learning and storing information in plastic states. Here, a typical mechanical pendulum is modified to have an adjustable rod length to create a pendulum adaptive frequency oscillator. Since the resonance frequency of the pendulum is a function of the rod length, this allows the pendulum to learn and encode frequency information from an external source. An experimental pendulum adaptive frequency oscillator is designed and constructed, and its performance is compared to numerical simulations. This nonlinear pendulum was approximated as a Duffing oscillator through the method of multiple scales to determine the physical constants of the experiment by using a curve fit. Utilizing the pendulum adaptive frequency oscillator’s dynamics, this system is able to learn a resonance condition and store this information in the rod length. This causes the system to seek resonance, even with considerable nonlinearity. As pendulums can be used to harvest energy, this type of adaptation could be used to further exploit vibratory energy sources.}, journal={Mechanical Systems and Signal Processing}, author={Li, XiaoFu and Kallepalli, Pawan and Mollik, Tushar and Ul Shougat, Md Raf E and Kennedy, Scott and Frabitore, Sean and Perkins, Edmon}, year={2022}, month={Nov}, pages={109361} }
 @article{li_ul shougat_kennedy_fendley_dean_beal_perkins_2021, title={A four-state adaptive Hopf oscillator}, volume={16}, ISSN={["1932-6203"]}, DOI={10.1371/journal.pone.0249131}, abstractNote={Adaptive oscillators (AOs) are nonlinear oscillators with plastic states that encode information. Here, an analog implementation of a four-state adaptive oscillator, including design, fabrication, and verification through hardware measurement, is presented. The result is an oscillator that can learn the frequency and amplitude of an external stimulus over a large range. Notably, the adaptive oscillator learns parameters of external stimuli through its ability to completely synchronize without using any pre- or post-processing methods. Previously, Hopf oscillators have been built as two-state (a regular Hopf oscillator) and three-state (a Hopf oscillator with adaptive frequency) systems via VLSI and FPGA designs. Building on these important implementations, a continuous-time, analog circuit implementation of a Hopf oscillator with adaptive frequency and amplitude is achieved. The hardware measurements and SPICE simulation show good agreement. To demonstrate some of its functionality, the circuit’s response to several complex waveforms, including the response of a square wave, a sawtooth wave, strain gauge data of an impact of a nonlinear beam, and audio data of a noisy microphone recording, are reported. By learning both the frequency and amplitude, this circuit could be used to enhance applications of AOs for robotic gait, clock oscillators, analog frequency analyzers, and energy harvesting.}, number={3}, journal={PLOS ONE}, author={Li, XiaoFu and Ul Shougat, Md Raf E. and Kennedy, Scott and Fendley, Casey and Dean, Robert N. and Beal, Aubrey N. and Perkins, Edmon}, year={2021}, month={Mar} }