High Performance Model Predictive Control for PMSM by Using Stator Current Mathematical Model Self-Regulation Technique

High Performance Model Predictive Control for PMSM by Using Stator Current Mathematical Model Self-Regulation Technique

High Performance Model Predictive Control for PMSM by Using Stator Current Mathematical Model Self-Regulation Technique

Abstract:

Excellent control performance and high robustness under different operating conditions are primary purposes pursued by many model predictive control algorithms. As a model-based control algorithm, the accuracy of the stator current mathematical model has a significant impact on the control performance of the predictive current control (PCC) method. To improve the current tracking accuracy and the robustness against parameter variation, a stator current mathematical model self-regulation strategy, which uses stator current prediction error to calculate parameter changes and design a parameter variation compensation strategy to correct the mathematical model in real-time at each control cycle, based on PCC algorithm is proposed to pursue desired performance. Consequently, the elimination of stator current prediction error, high controlled quality, and better robustness have been achieved in the proposed method. The comparative simulation and experiment results validate the superiority of the proposed method.

F. Wang, K. Zuo, P. Tao and J. Rodríguez, “High Performance Model Predictive Control for PMSM by Using Stator Current Mathematical Model Self-Regulation Technique,” in IEEE Transactions on Power Electronics, vol. 35, no. 12, pp. 13652-13662, Dec. 2020. doi: 10.1109/TPEL.2020.2994948
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9094347&isnumber=9158598