Closed-Loop Neural Network-Based NMES Control for Human Limb Tracking

Abstract

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.