Model-Free Predictive Current Control of PMSM With Online Current Gradient Updating Using Ultra-Local Model

Abstract

Parameter mismatches can significantly degrade the control performance of model predictive current control (MPCC). To improve the parameter robustness of MPCC for permanent magnet synchronous motor (PMSM), this paper presents a model-free predictive current control algorithm based on online current gradient updates. First, an online current gradient update mechanism utilizing an ultra-local model is proposed, which effectively solves the stagnation issue associated with traditional current gradient update methods and eliminates error amplification caused by division operations. Furthermore, to handle the sensitivity of the voltage gain part in the conventional ultra-local model to inductance variations, a method is proposed to update both the voltage gain and dynamic parts of the ultra-local model in real-time using Kalman filtering. This method enhances the parameter robustness of model-free current prediction control. Finally, the proposed algorithm was simulated and experimentally validated, demonstrating the effectiveness of the control strategy.