A Novel Double-Vector Model-Predictive Current Control for PMSM With Low Computational Burden and Switching Frequency

The existing double-vector (DV) model-predictive control for a permanent magnet synchronous motor (PMSM) selects the optimal voltage vector (OVV) combination by traversing 12 alternative voltage vector combinations, which requires calculating the cost function 24 times. Also, the switching states between adjacent cycles are not optimized. To address these issues, a new DV model-predictive current control method is proposed in this article. First, the first OVV is selected from eight basic voltage vectors by a cost function. The predicted current errors between the predicted currents corresponding to the first OVV and the actual currents are used as the basis for selecting the second OVV. According to the principle of minimizing the number of switchings, there are only three alternative vectors for the second OVV. Therefore, the number of calculations of the cost function is decreased to 11, greatly reducing the computational burden. In addition, this article incorporates the objective of reducing the switching frequency into the cost function, resulting in a significant reduction of the switching frequency. The effectiveness of the proposed method is verified by experimental results on a 2.7-kW PMSM drive platform.