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

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 predictiv...

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Veröffentlicht in:IET power electronics Jg. 12; H. 11; S. 2895 - 2903
Hauptverfasser: Xu, Yanping, Ding, Xianhua, Wang, Jibing, Wang, Chen
Format: Journal Article
Sprache:Englisch
Veröffentlicht: The Institution of Engineering and Technology 18.09.2019
Schlagworte:
angular velocity control
computational complexity
electric current control
invertors
machine control
observers
optimal voltage vector combination
permanent magnet motors
permanent magnet synchronous motor
predictive control
Research Article
robust control
robust three‐vector‐based low‐complexity model
second‐order sliding‐mode observer
supertwisting‐algorithm‐based
synchronous motor drives
synchronous motors
three‐vector‐based model predictive current control
torque control
variable structure systems
machine control
optimal voltage vector combination
angular velocity control
second-order sliding-mode observer
robust three-vector-based low-complexity model
torque control
synchronous motors
electric current control
invertors
three-vector-based model predictive current control
permanent magnet synchronous motor
robust control
synchronous motor drives
predictive control
permanent magnet motors
supertwisting-algorithm-based
observers
variable structure systems
computational complexity
ISSN:1755-4535, 1755-4543
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Beschreibung
Zusammenfassung: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.
ISSN:1755-4535
1755-4543
DOI:10.1049/iet-pel.2018.5750