Torque Ripple Minimization of Switched Reluctance Motor Based on Adaptive Fuzzy-Neural Networks

The torque ripple of switched reluctance motor limits its application in speed regulation fielle.This text uses the adaptive fuzzy-neural networks to the learning of its static torque inverse model off line.Then according to the torque distribution function,each phase torque was distributed,and optimal current profile was real-time gained by fuzzy-neural networks on line,which result in low ripple torque control of switched reluctance motor.The simulation result proves validity of this method.