摘要
磁悬浮开关磁阻电动机作为一个多变量、非线性和强耦合的系统,其控制系统的设计非常复杂。对于磁悬浮开关磁阻电动机来说,得到径向力和平均转矩的先验知识是实现电机闭环控制的基本条件。基于基本电磁场理论,论文给出磁悬浮开关磁阻电动机的径向力模型,对该模型进行可逆性分析,证明该系统可逆,应用神经网络逆系统方法实现径向力的动态解耦,以便达到高性能的控制目的,仿真结果验证了方法的可行性。
A bearingless switched reluctance motor is a multi variable, nonlinear, high coupling system, and the design of its control system is very complicated. For closed loop control of a bearingless switched reluctance motor, radial force and average torque are basic. In this paper, the radial force model of a bearingless switched reluctance motor is given, based on basic electromagnetism theory. Reversibility of the model is proved. Using neural network inverse system, radial force control is decoupled, so high performance control is received. The feasibility of this method is validated by some results of simulation.
出处
《电工技术学报》
EI
CSCD
北大核心
2005年第9期39-43,共5页
Transactions of China Electrotechnical Society
基金
国家自然科学基金资助项目(60174052)
江苏省自然科学基金资助项目(BK2003049)
关键词
磁悬浮电动机
开关磁阻电动机
神经网络逆系统
解耦
Bearingless motor, switched reluctance motors, neural network inverse system,decoupling
