Model Predictive Control of Permanent Magnet Synchronous Motor Based on Parameter Identification and Dead Time Compensation

A model predictive control method for permanent magnet synchronous motor based on parameter identification and dead time compensation is proposed to solve the problems of poor parameter robustness and large current errors. In this method, the prediction model is firstly established based on the mathematical model of the permanent magnet synchronous motor. After that, the current error caused by the parameter change in the prediction model and the current harmonics caused by the dead time effect are basically analyzed theoretically. Then, the adaptive linear neural network algorithm is proposed to identify the motor parameters and applied to the prediction model, and the harmonic components are filtered out using the adaptive linear neural network algorithm. The recursive least squares algorithm is used to quickly update the system weights to improve the dead time compensation control effect. Finally, the effectiveness and correctness of the proposed algorithm are verified on the experimental platform. The experimental results show that the predictive control method of permanent magnet synchronous motor model based on parameter identification and dead time compensation can effectively reduce the current error of the control system and accelerate the dynamic response of the speed. © 2022, Electromagnetics Academy. All rights reserved.