Model Predictive Control of Permanent Magnet Synchronous Motor Based on Recursive Least Square Parameter Identification

Model predictive control (MPC) has simple control structure and can achieve dynamic optimization control under constraint conditions. Due to the predictive control based on the system model, MPC has a high requirement for the accuracy of model parameters. This paper proposes a recursive least squares method based on the mathematical model of the motor for online parameter identification of permanent magnet synchronous motor (PMSM), which addresses the issue of sudden parameter changes that affect control effectiveness during operation. Through online identification, it is possible to effectively track changes in motor parameters and obtain real-time and accurate motor models. In addition, the updated motor model can serve as a predictive model for MPC. Thus, MPC can achieve more dynamic accurate prediction. Finally, the simulation is carried out to verify the effectiveness of the method that is proposed in this paper.