A Virtual-Vector Based Model-Free Predictive Current Control for PMSM Drives with Adaptive Control Gain

Model predictive current control (MPCC), owing to its straightforward design and convenient multi-objective optimization, has been widely adopted in applications demanding high dynamic performance. However, the conventional MPCC suffers from poor current steady-state performance and severe parameter dependence. To address these issues, this paper proposes a virtual-vector based model-free predictive current control (MFPCC) scheme for permanent magnet synchronous machine (PMSM) drives with adaptive control-gain. The proposed approach is developed based on the ultra-local model (ULM) concept to simplify the control structure and enhance robustness. The disturbance is observed by a linear extended state observer (LESO) and the effect of control-gain deviation on disturbance observation is analyzed. In addition, a control gain adaptive method is introduced to weaken the high-frequency components of the integrated disturbance, which can further improve the performance of observer. Furthermore, the virtual-vector control set is built where symmetrical vector sequences are included to reduce torque ripple. An improved optimization strategy is also developed that reduces computation and improves steady-state performance. Comprehensive experimental results confirm the effectiveness and superiority of the proposed method in terms of steady-state performance, robustness, and computational burden.