Robust three-vector-based low-complexity model predictive current control with supertwisting-algorithm-based second-order sliding-mode observer for permanent magnet synchronous motor.

Yanping Xu; Xianhua Ding; Jibing Wang; Chen Wang

This study presents a robust three-vector-based low-complexity model predictive current control with supertwistingalgorithm- based second-order sliding-mode observer for permanent magnet synchronous motor (PMSM). First, to reduce the computational complexity of the three-vector-based model predictive current control, the optimal voltage vector combination is directly determined by the sector of desired voltage vector. Second, a supertwisting-algorithm-based second-order sliding-mode observer is designed to observe the lump disturbance caused by model mismatch and unmodelled dynamics. The estimated lump disturbance is considered as the compensation to the original PMSM model to reduce steady-state current error, which improves the robustness of the three-vector-based model predictive current control. Finally, the effectiveness of the proposed method is verified by experiments on a two-level-inverter-fed PMSM drive platform. Experimental results prove that, compared with three-vector-based model predictive current control, the proposed method can reduce the computational complexity and enhance robustness against motor parameters variation.

PREDICTIVE control systems; PERMANENT magnet motors; SLIDING mode control; SPIN-spin interactions; PREDICTION models; COMPUTATIONAL complexity

IET Power Electronics (Wiley-Blackwell), 2019, Vol 12, Issue 11, p1

1755-4535

Academic Journal

10.1049/iet-pel.2018.5750