FCS-MPC of IPMSM Based on Online Parameter Identification Under Demagnetization Condition of Permanent Magnet

In permanent magnet synchronous motors (PMSM), the permanent magnet can easily demagnetize under high temperature and high-speed conditions due to the action of demagnetizing current. The stator resistance and stator inductance also vary with the operation of the motor. These parameter variations hinder the optimal performance of finite control set model predictive control (FCS-MPC). This paper proposes a Finite Control Set Model Predictive Current Control (FCS-MPCC) based on parameter identification methods for interior permanent magnet synchronous motors (IPMSM), which can implement model current predictive control under conditions of parameter variations. The recursive least squares (RLS) method with a forgetting factor was used to achieve online real-time identification of four electrical parameters, namely stator resistance, d-axis inductance, q-axis inductance, and magnetic flux. The identified parameters are updated in real-time into the model predictive control, achieving FCS-MPC under conditions of magnet demagnetization. Simulation results show that, compared to conventional FCS-MPC strategies based on motor nominal parameters, the proposed method reduces current prediction errors and effectively reduces motor speed fluctuation under conditions of magnet demagnetization. This paper contributes to expanding the application of IPMSM under conditions leading to magnet demagnetization, such as high temperature or high-speed states.