Research On Parameter Identification And Model Predictive Control Of Permanent Magnet Synchronous Motor

Abstract:The change of stator and rotor parameters in Permanent magnet synchronous motor (PMSM) affects its control performance seriously. In this paper, parameters identification and model predictive control for PMSM are studied.To solve the problem of selecting forgetting factor in forgetting factor recursive least squares parameter identification method, a new method is proposed in this paper. In the new method, the forgetting factor is set as the median of each segment which can change with errors. Then, the parameter identification model is built. Based on the model, the identification results of three methods, which include forgetting factor recursive least square method, linear time-varying forgetting factor recursive least square method and proposed segmental median forgetting factor recursive least square method, are compared and analyzed. The results show that using the proposed method to identify parameters can get most accurate identification values and shortest identification time.To resist the influences of parameters varying and external load disturbance, an improved model predictive control strategy for PMSM is proposed. Voltage feed-forward compensation module is added to decouple the d-axis current and q-axis current. The current loop model predictive controller and the speed loop model predictive controller are designed by using first order differential increment method respectively. Further, the incremental expressions of model predictive controllers are obtained according to a designed performance index function. Finally, the control system simulation model of PMSM is set up. The simulation results show that compared with PI controller, model predictive controller, which can suppress the influence of parameters varying and external load disturbance, is of stronger robustness.