Modelling of switched reluctance motor based on variable structure fuzzy-neural networks

Abstract

Switched reluctance motors (SRM) are almost always operated within the saturation region for a very large operation region. This yields very strong nonlinearities, which makes it very difficult to derive a comprehensive mathematical model for the behavior of the machine. This paper presents the variable structure fuzzy-neural networks model of SRM. Based on the Takagi-Sugeno fuzzy-neural networks, a variable structure and step learning arithmetic was presented. Then the fuzzy-simulation results show that this method is more precise and less time-consuming for convergence than BP neural networks model.