Iterative Learning Control Strategy for Functional Neuromuscular Stimulation

An iterative learning controller (ILC) is proposed for the tracking control of functional neuromuscular stimulation (FNS) system performing the given task repeatedly. A P-type ILC updating law assisted by PD closed-loop controller is suggested for a simpler implementation. This kind of learning from repetitions control strategy supplies strong robustness in tracking control of uncertain time-varying FNS systems, which is essential for the adaptation and customization of FNS applications. Nonlin-ear muscle recruitment, linear muscle dynamics in force generation, and multiplicative nonlinear torque-angle and torque-velocity scaling factors are considered in the electrically stimulated muscle model for the simulation studies. An one-segment planar system with passive constraints on joint movement is taken as the skeletal model. Simulation results indicate that the control scheme of this paper is promising for FNS system control.