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This brief examines the use of a learning control method in a passivity-based framework to control a motorized cycle-rider system with functional electrical stimulation (FES) of the quadriceps muscle groups. FES cycling with motorized assistance has been used in the rehabilitation of people with neurological conditions. The concepts of adaptation and passivity are explored to compensate for the uncertain nonlinear time-varying dynamics of the motorized FES cycle-rider system. The system is modeled as a closed-loop feedback, state-dependent switched system such that in each cycle, the quadriceps muscle groups produce the functional torque and the electric motor provides assistance as needed. The output strictly passive feature of the closed-loop system is proven by considering a learning control input. Then, an adaptive update law, based on iterative learning control, is developed to guarantee the convergence of the cadence tracking error. Experimental results from seven able-bodied participants are presented and discussed to demonstrate the effectiveness of this approach. The average cadence tracking error is 0.00 ± 2.47 rpm for the desired trajectory of 50 rpm.

Passivity-Based Iterative Learning Control for Cycling Induced by Functional Electrical Stimulation With Electric Motor Assistance