High-order iterative learning control of functional neuromuscular stimulation systems

A high-order iterative learning controller (ILC) is proposed for the tracking control of a functional neuromuscular stimulation (FNS) system. The high-order ILC scheme may improve the transient control performance along the iteration number direction. The proposed discrete-time high-order P-type ILC updating law and the PD-type closed-loop controller provide strong robustness in tracking control of the uncertain time-varying FNS systems, which is essential for the adaptation and customization of FNS applications. Moreover, control input saturations are considered in the convergence analysis. Simulation results based on an electrically stimulated muscle model and a single joint skeletal model are presented to show the effectiveness of the control scheme proposed. A set of experimental results are briefly presented to support the simulation results.

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