Optimization of FLC parameters for optimal control of FES-assisted elliptical stepping exercise using GA and PSO

Abstract

This paper presents the parameter optimization of the fuzzy logic controller (FLC) for the Functional Electrical Stimulation (FES)-assisted elliptical stepping exercise. The FLC is used to control the cadence of the elliptical stepping exercise for smooth exercise movement. Genetic algorithm (GA) and particle swarm optimization (PSO) are used to optimize the parameters of the FLC. Both algorithms are implemented in Matlab and simulated with the dynamic model of the elliptical stepping exercise. In the performance analysis, the GA has faster convergence compared to the PSO where both converged at 40th and 51st iterations, respectively. The root mean square error (RMSE) for the GA and PSO are 7.873 rpm and 7.087 rpm respectively showing that the PSO has better performance in terms of the RMSE. Both techniques also have shown good performance in stepping cycle completion. The use of the GA and PSO had led towards more efficient FLC control for the FES-assisted elliptical stepping exercise.