An Adaptive FNN Control for Torque-Ripple Reduction of SR Motor Drive

The purpose of this paper is to implement a novel approach to learning control for torque-ripple reduction of switched reluctance motor (SRM) using an adaptive fuzzy neural network (AFNN) control. First, the dynamic models of a SRM drive system are builted though SRM experimental tests and parameters measurements. Then, in order to reduce torque ripple, an AFNN speed control system that combined FNN and compensated control with adaptive law is developed to control SRM drive system. The AFNN control system produces smooth torque up to the motor base speed. Finally, the effectiveness of the proposed control scheme is demonstrated by experimental results.