Simplified robust two-step model predictive control for permanent magnet synchronous motor

Abstract

Traditional finite control set model predictive control (FCS-MPC) selects an optimal voltage vector in one control cycle. However, considering multiple control cycles, it cannot be proved that the voltage vector is optimal, and the uncertainty of PMSM parameters seriously affects the prediction accuracy. In order to balance the relationship between switching frequency, steady-state performance, and robustness, a multi-step robust FCS-MPC is proposed. Firstly, an incremental prediction model is established to eliminate the flux linkage of permanent magnets. The inductance parameters in the incremental model are identified online based on the observer and inductance extraction algorithm. Then, based on the principle of no-beat, the candidate voltage vector in two control periods is simplified to get the candidate voltage vector, and the cost function is used to determine the optimal voltage vector again. Finally, the proposed FCS-MPC method is compared with the traditional FCS-MPC method. The experimental results show the effectiveness of FCS-MPC strategy.