基于自适应模糊神经网络的开关磁阻电机转矩脉动抑制  

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

作　　者：丁文[1] 周会军[1] 鱼振民[1] 

机构地区：[1]西安交通大学,西安710049

出　　处：《微电机》2005年第6期3-5,9,33,共5页Micromotors

摘　　要：应用自适应模糊神经网络对开关磁阻电动机静态转矩逆模型进行离线学习,学习完成之后,根据转矩分配函数对各相转矩进行分配,利用模糊神经网络实时优化出期望转矩所需要的相电流波形,从而实现开关磁阻电机的低转矩脉动控制。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 functlon,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.

关 键 词：开关磁阻电动机 模糊神经网络 转矩 相电流 

分 类 号：TM352[电气工程—电机]