Model-Free Predictive Current Control of Permanent Magnet Synchronous Motor Based on Estimation of Current Variations

Model-free predictive current control (MFPCC) completely separates from the motor model, and only needs to read the current variations in the lookup tables to predict future currents, thus having strong parameter robustness. However, the stagnation of current variations is a core issue that needs to be addressed in existing MFPCCs. Therefore, this article proposes a more effective MFPCC strategy of permanent magnet synchronous motor. First, an advanced strategy is proposed to estimate current variations using a variant of the ultralocal model, where the local variables in the model are provided in real-time by sliding mode observer. This method can easily extract the current variations corresponding to all voltage vectors from the measured current variations caused by the voltage vectors applied in the previous period. Secondly, an anti-stagnation mechanism with higher reliability and better performance was designed. This mechanism effectively improves prediction accuracy by utilizing a pair of antiphase vectors and corresponding current variations, avoiding update stagnation. Since non-optimal vectors are not forced to be applied, this anti-stagnation mechanism has no impact on control performance. Finally, through comparative experiments on multiple predictive current control schemes, it is demonstrated that the proposed MFPCC has improved both steady-state and dynamic performance.