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.
- Dynamic Systems and Control Division
Model Predictive Control-Based Dynamic Control Allocation in a Hybrid Neuroprosthesis
Kirsch, NA, Alibeji, NA, & Sharma, N. "Model Predictive Control-Based Dynamic Control Allocation in a Hybrid Neuroprosthesis." Proceedings of the ASME 2014 Dynamic Systems and Control Conference. Volume 3: Industrial Applications; Modeling for Oil and Gas, Control and Validation, Estimation, and Control of Automotive Systems; Multi-Agent and Networked Systems; Control System Design; Physical Human-Robot Interaction; Rehabilitation Robotics; Sensing and Actuation for Control; Biomedical Systems; Time Delay Systems and Stability; Unmanned Ground and Surface Robotics; Vehicle Motion Controls; Vibration Analysis and Isolation; Vibration and Control for Energy Harvesting; Wind Energy. San Antonio, Texas, USA. October 22–24, 2014. V003T43A003. ASME. https://doi.org/10.1115/DSCC2014-6133
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