Predictor-Based Tracking For Neuromuscular Electrical Stimulation

TL;DR

This paper introduces a predictor-based hybrid control method for neuromuscular electrical stimulation that ensures exponential convergence, robustness to sampling perturbations, and adherence to physical state constraints.

Contribution

It presents a novel predictor-based control scheme that improves tracking accuracy and robustness in neuromuscular electrical stimulation systems.

Findings

Tracking error converges exponentially to zero

System exhibits robustness to sampling schedule perturbations

State constraints imposed by the physical system are satisfied

Abstract

A new hybrid tracking controller for neuromuscular electrical stimulation is proposed. The control scheme uses sampled measurements and is designed by utilizing a numerical prediction of the state variables. The tracking error of the closed-loop system converges exponentially to zero and robustness to perturbations of the sampling schedule is exhibited. One of the novelties of our approach is the ability to satisfy a state constraint imposed by the physical system.