Intelligent control for torque-ripple minimization in switched reluctance motor

Abstract

A fuzzy control scheme and adaptive neuro fuzzy inference system for torque-ripple minimization of switched reluctance machines is presented. The fuzzy parameters are initially chosen randomly and then adjusted to optimize the control. The controller produces smooth torque up to the motor base speed. The torque is generated over the maximum positive torque-producing region of a phase. This increases the torque density and avoids high current peaks. The controller is robust toward errors in the rotor position information, which means inexpensive crude position sensors can be used. This paper presents a novel method of controlling the motor currents to minimise the torque ripple based on a fuzzy logic current compensator. In the proposed controller, a compensating signal is added to the output in a current-regulated speed control-loop. The compensating signal is learned prior to normal operation, in a self-commissioning run, but the neuro-fuzzy methodology is also suitable for on-line self-learning implementation, for continuous improvement of the compensating signal presented. The controller shows good response in both cases. Performance of the proposed strategy is verified by simulation using MATLAB.