Learning rate functions in CMAC neural network based control for torque ripple reduction of switched reluctance motors

This paper presents a novel approach to adapting the weights of a CMAC neural network-based controllers for torque ripple reduction in switched reluctance motors. The proposed method modifies the conventional LMS algorithm using a varying learning rate which, for the present application, is defined as a function of the rotor angle of the motor under control. Simulation results demonstrate that developing CMAC network based adaptive controllers following this approach affords lower torque ripple with high power efficiency, whilst offering rapid learning convergence in system adaptation