Abstract

As the updating frequency decreases, the stagnation effect poses a challenge to the accuracy of model-free predictive control (MFPC) based on signal gradients. To mitigate this issue from the perspective of sampled data, a low-stagnation model-free predictive current control (MF-PCC) strategy is proposed in this article and applied in permanent magnet synchronous motor (PMSM) drives. Recognizing that some elements of the sampled data do not contribute to accurately depict the motion characteristics and operational states of the system, a frequency-converting double second-order generalized integrator (FC-DSOGI) structure is improved and utilized to extract these elements and reinject them by a random gain, thereby generating a coercive difference to reduce the stagnation effect. Furthermore, fuzzy logic is developed to determine the optimal gains for the control law, thereby enhancing the control performance. The generalized universal model (GUM) is chosen as an illustrative case. Through experimental validation, the effectiveness of the proposed method is demonstrated with enhancements in current quality and model accuracy, alongside enhanced robustness. Moreover, it showcases the superiority of the low-stagnation over conventional control strategies.