Virtual vector permanent magnet synchronous motor model predictive control based on discrete space gridding

To further enhance the prediction accuracy and mitigate issues such as large current ripples, subpar steady-state precision, and excessive computational load in the Model Predictive Control (MPC) of Permanent Magnet Synchronous Motors (PMSM), this paper proposes a novel control strategy termed Discrete Space Grid-based Virtual Voltage Vector Model Predictive Current Control (DSG-VV-MPC). This methodology augments the prediction accuracy by discretizing the control space, which leads to an increased number of potential virtual vectors that more closely approximate the actual voltage vectors. Furthermore, the implementation of a preprocessing technique reduces the number of candidate vectors and the associated optimization iterations, consequently diminishing the computational burden. The findings from both simulation and experimental analyses indicate that the DSG-VV-MPC method significantly outperforms the conventional three-sector VV-MPC approach, resulting in improved steady-state performance and validating the effectiveness and feasibility of the proposed approach.