中国电机工程学报2011，Vol.31Issue(30)：117-123,7.
磁悬浮开关磁阻电机的神经网络逆解耦控制
Decoupling Control of Bearingless Switched Reluctance Motor With Neural Network Inverse System Method
摘要
Abstract
In view of complicated non-linearity,coupling,and magnetic saturation,it is very difficult to gain an accurate mathematic model and realize decoupling for a bearingless switched reluctance motor（BSRM）.So after analyzing the magnetic field and force characteristics with finite element method,a novel mathematical model was computed.This model could be fit for both linear and saturated state,and even meet reversible requirement.Then a neural network inverse model was established to decouple BSRM.Lastly,three closed-loop controllers were designed for pseudo-linear systems.Experimental results based on dSPACE system validated this method.It can remedy the shortcomings of those existing decoupling means based on non-saturation hypothesis,which are not suitable for magnetic-saturated work condition,and can provide more reliable theoretical bases for running state analysis,motor design,and control strategy study.关键词
磁悬浮电机/开关磁阻电机/解耦/神经网络逆Key words
bearingless motor/switched reluctance motor/decoupling/neural network inverse system分类
信息技术与安全科学引用本文复制引用
孙玉坤,周云红,嵇小辅..磁悬浮开关磁阻电机的神经网络逆解耦控制[J].中国电机工程学报,2011,31(30):117-123,7.基金项目
国家自然科学基金项目（61074019, 60774044) （）
江苏高校优势学科建设工程资助项目 （）
