RETRACTED ARTICLE: Integral Backstepping Control of LPMSM Drive System Using Revised Recurrent Fuzzy NN and Mended Particle Swarm Optimization

Abstract

A linear permanent magnet synchronous motor (LPMSM) drive system is keeping in many nonlinear effects such as the external load force, the flux saturation, the cogging force, the column friction and Stribeck force, and the parameters variations. Due to the uncertainty effects the existing linear controllers can not achieve better control performances for the LPMSM drive system. To raise robustness under occurrence of uncertainty, the integral backstepping control system with hitting function is proposed for controlling the LPMSM drive system in accordance with the Lyapunov function. To improve larger chattering phenomenon under uncertainties effects, the integral backstepping control system with revised recurrent fuzzy neural network (RRFNN) and mended particle swarm optimization (MPSO) is proposed to operate the LPMSM drive system to raise robustness of system. The RRFNN is used to estimate the value of the external lumped force uncertainty. Moreover, the error compensation control with the error compensation mechanism is proposed to compensate the minimum reconstructed error of the error estimation law. Besides, four variable learning rates in the weights of the RRFNN are regulated by virtue of MPSO with segment regulation to speed-up parameter’s convergence. Finally, comparative performances through some tentative upshots are verified that the integral backstepping control system by virtue of RRFNN with MPSO has better control performances than those of the proposed methods for the LPMSM drive system.