Anti-Stagnation-based Model-Free Predictive Control for PMSM Drives with Control Compensation

Model-free predictive control (MFPC) operates entirely independently of physical models and parameters by leveraging a data-driven model. However, the processes of modeling and updating these models demand high-quality sampled data, with stagnation and its negative effect posing significant obstacles to the advancement of this technology. To overcome this challenge, this paper proposes an anti-stagnation-based MFPC specifically for permanent magnet synchronous motor (PMSM) drives. This approach features a notch structure designed to extract specific frequency band harmonics generated by the control strategy and then inversely inject them into the sampled data to create data gradients. This method aims to minimize the risk of stagnation and alleviate its adverse effects, while also incorporating control compensation. At the theoretical level, a comprehensive analysis of the method’s stability and robustness is conducted. Experimental results show that, compared to the strategy without an anti-stagnation, the proposed method offers superior current quality and prediction accuracy, providing a novel and effective solution for high-performance control of PMSM drives in complex environments.