基于卡尔曼滤波和神经网络的PMSM参数辨识

Identification of PMSM Based on EKF and Elman Neural Network

永磁同步电机(PMSM)是一种非线性、强耦合的控制对象,电机参数的变化加大了其控制难度.因此,参数辨识对于其闭环控制系统的稳定运行有着重大的意义.文中针对这一非线性、强耦合的模型,研究了一种基于扩展卡尔曼滤波(EKF)和El man神经网络(El man NN)的永磁同步电机参数Rs,ψd和ψq的辨识方法.仿真结果表明,该方法具有很快的收敛速度,能很精确地辨识PMSM的Rs,ψd和ψq,该网络具有良好的泛化能力,在变速变负载等复杂情况下也适用.

Permanent magnet synchronous motor（PMSM） is a complex plant to control,due to its high nonlinearity and strong coupling.At the same time,the variation of motor parameters make this problem more serious.So,parameter identification of PMSM seems to be important for the two closed-loop vector control system.To solve this problem,a new method combining elman neural network（ENN） and modified extended kalman filter（EKF） is studied in this paper.The approach of identifying Rs,ψd and ψq is discussed.Simulation results show that it has lots of advantages such as high precision,fast convergence and excellent generalization ability and it is suitable for variable speed and load disturbance,even more complex circumstance.