A new method named Windowed Least Square (WLS) to test main parameters of Permanent Magnet Synchronous Motor (PMSM) is proposed in this paper. Compared with Extended Kalman Filter (EKF) & Elman neural network and Recursive Least Square (RLS), WLS guarantees identification accuracy and excellent timeliness, and the issue of data saturation of RLS can be avoided. The PMSM model is built combining on-line parameter identification with Active Disturbance Rejection Control (ADRC) to improve the control performance of PMSM. The simulation results demonstrate that the performance of ADRC system using online estimation strategy is better than that of the system using PID method.
Real-time accuracy of the permanent magnet synchronous motor (PMSM) parameter directly affects the performance of the servo system. In this paper, the model reference adaptive parameter identification method, the parameters of permanent magnet synchronous motor running as the actual model , the mathematical model derived from the equation of state in the d, q coordinate system as the theoretical model , in order to calculate the real-time stator resistance , inductance and rotor flux value . Thereby increasing the accuracy of the vector control .Using simulink simulation results show that, The system can quickly and accurately calculate the real-time parameters and the applied control.
FAST BACKFIRE DOUBLE ANNEALING PARTICLE SWARM OPTIMIZATION ALGORITHM FOR PARAMETER IDENTIFICATION OF PERMANENT MAGNET SYNCHRONOUS MOTOR