OPtimizing the switching angles of SRM Using Adaptive Neuro-Fuzzy Controller

This paper presents an application of adaptive neuro-fuzzy controller for optimizing the switching angles of SRM. The proposed adaptive neuro-fuzzy controller has the advantages of expert knowledge of the fuzzy inference system and the learning capability of neural networks. This article is a novel control strategy to improve the power factor of switched reluctance motor drives through optimizing the turn-on angle and the turn-off angle by using the adaptive neuro-fuzzy controller. The simulation results validate the proposed control strategy. The proposed schemes are suitable for both small and large ratings of SRM drives.