Open AccessRobust three‐vector‐based low‐complexity model predictive current control with supertwisting‐algorithm‐based second‐order sliding‐mode observer for permanent magnet synchronous motor
Author(s) -
Xu Yanping,
Ding Xianhua,
Wang Jibing,
Wang Chen
Publication year - 2019
Publication title -
iet power electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.637
H-Index - 77
eISSN - 1755-4543
pISSN - 1755-4535
DOI - 10.1049/iet-pel.2018.5750
Subject(s) - control theory (sociology) , robustness (evolution) , vector control , model predictive control , state observer , sliding mode control , computer science , observer (physics) , computational complexity theory , voltage , algorithm , engineering , induction motor , artificial intelligence , control (management) , physics , nonlinear system , quantum mechanics , electrical engineering , biochemistry , chemistry , gene
This study presents a robust three‐vector‐based low‐complexity model predictive current control with supertwisting‐algorithm‐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.