Decoupling Control of Bearingless Permanent Magnet-type Synchronous Motor Based on Artificial Neural Networks Inverse System Method

摘要：

A bearingless permanent magnet-type synchronous motor (BPMSM) is a complicated nonlinear strong-coupled system.The decoupling control among the electromagnetic torque and radial suspension forces,and the dynamic decoupling control between the two orthogonal suspension forces are the base of stable operation for a BPMSM.In the paper,the mathematics models of complex-controlled object which consists of two Park inverse transformations,two Clark inverse transformations,two current following inverters and load model of BPMSM are given.The reversibility of the complex-controlled object is proved.Combining the artificial neural networks (ANN) inverse which consists of a static ANN and five integrators with the complex-controlled object,the control system is decoupled into two independent 2-order linear subsystems and a 1-order linear subsystem,namely they are two displacement subsystems and a rotor speed one.And then it is easy to design the close-loop linear regulators to control each of the subsystems.The simulation test results have shown that the strong robustness,the good static and dynamic decoupling performance can be achieved using the proposed strategy.

关键词： bearingless permanent magnet-type synchronous motor (BPMSM) complex-controlled object artificial neural network (ANN) inverse system decoupling control

作者: Xiao-dong Sun Huang-qiu Zhu Wei Pan

作者单位: School of electrical and information engineering,Jiangsu University,Zhenjiang 212013,China

会议类型: 国际会议

会议名称: International Conference on Modelling,Identification and Control(模拟、鉴定、控制国际会议)

会议地点: 上海

会议语种:英文

在线出版日期: 2008-06-29（万方平台首次上网日期，不代表论文的发表时间）