Switched reluctance motor circuit drive system using adaptive nonlinear backstepping control with mended recurrent Romanovski polynomials neural network and mended particle swarm optimization

Abstract

A switched reluctance motor (SRM) circuit drive system caused many nonlinear effects due to convex construction. The linear control methods were hard to achieve good performance for the SRM circuit drive. The adaptive nonlinear backstepping control system using switching function is proposed for controlling the SRM drive system to obtain good performance. To reduce chattering of control effort, the adaptive nonlinear backstepping control system using adaptive law is proposed to estimate the required lumped uncertainty. When the inertia of the counterweight is varying, this proposed method cannot get a satisfactory performance. The adaptive nonlinear backstepping control system using mended recurrent Romanovski polynomials neural network with adaptive law and error-estimated law is proposed for controlling the SRM drive system to raise robustness of the SRM drive system. Furthermore, two variable learning rates in the mended recurrent Romanovski polynomials neural network are adopted by using mended particle swarm optimization (PSO) algorithm to speed up parameter's convergence. Finally, comparative performances through some experimental results are verified that the proposed control system has better control performances than those of the proposed methods for the SRM drive system. KEYWORDS adaptive backstepping control, particle swarm optimization, recurrent Romanovski polynomials neural network, switched reluctance motor

Micrometer Backstepping Control System for Linear Motion Single Axis Robot Machine Drive

“…Due to lesser computational complexity and faster convergent speed, the polynomials-function neural networks [16,17] have recently been proposed to reduce computational costs, while some parts of the polynomials-function neural networks were used to estimate some unknown parameters or the lumped uncertainty. Owing to uncertain actions, the control performance of the linear motion single axis robot machine drive can have a serious influence.…”