LAPSE:2023.36180
Published Article

LAPSE:2023.36180
Study on a Second-Order Adaptive Sliding-Mode Observer Control Algorithm for the Sensorless Permanent Magnet Synchronous Motor
July 4, 2023
Abstract
The control of a permanent magnet synchronous motor (PMSM) without a position sensor based on a sliding-mode observer (SMO) algorithm has a serious jitter problem in the process of motor phase tracking. A second-order adaptive sliding-mode observer algorithm was proposed, and the ideas and principles of the second-order sliding-mode observer algorithm based on the super-twisting algorithm were elaborated. In particular, adaptive estimation with the introduction of back-electromotive force (EMF) was investigated, and the Lyapunov stability criterion was used to determine the convergence properties of the algorithm. The results showed that the second-order adaptive sliding-mode observer algorithm had better jitter suppression and a better phase tracking performance than the traditional sliding-mode observer algorithm. The experimental results showed that when the motor velocity was 800 r/min, the velocity error of the second-order adaptive sliding-mode observer algorithm was 0.57 r/min and the position error was 0.018 rad, with accuracy improvements of 93.63% and 58.34%, respectively. When the motor velocity was 1000 r/min, the velocity error of the second-order adaptive sliding-mode observer algorithm was 0.94 r/min and the position error was 0.022 rad, with accuracy improvements of 90.55% and 55.10%, respectively. The jitter of the system was suppressed well, the curve of back-EMF was smoother, and the robustness of the system was high. Therefore, the second-order adaptive sliding-mode observer algorithm is more suitable for the position-sensorless control of a PMSM.
The control of a permanent magnet synchronous motor (PMSM) without a position sensor based on a sliding-mode observer (SMO) algorithm has a serious jitter problem in the process of motor phase tracking. A second-order adaptive sliding-mode observer algorithm was proposed, and the ideas and principles of the second-order sliding-mode observer algorithm based on the super-twisting algorithm were elaborated. In particular, adaptive estimation with the introduction of back-electromotive force (EMF) was investigated, and the Lyapunov stability criterion was used to determine the convergence properties of the algorithm. The results showed that the second-order adaptive sliding-mode observer algorithm had better jitter suppression and a better phase tracking performance than the traditional sliding-mode observer algorithm. The experimental results showed that when the motor velocity was 800 r/min, the velocity error of the second-order adaptive sliding-mode observer algorithm was 0.57 r/min and the position error was 0.018 rad, with accuracy improvements of 93.63% and 58.34%, respectively. When the motor velocity was 1000 r/min, the velocity error of the second-order adaptive sliding-mode observer algorithm was 0.94 r/min and the position error was 0.022 rad, with accuracy improvements of 90.55% and 55.10%, respectively. The jitter of the system was suppressed well, the curve of back-EMF was smoother, and the robustness of the system was high. Therefore, the second-order adaptive sliding-mode observer algorithm is more suitable for the position-sensorless control of a PMSM.
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Keywords
PMSM, position-sensorless control, second-order adaptive sliding-mode observer algorithm, super-twisting
Suggested Citation
Yao G, Cheng Y, Wang Z, Xiao Y. Study on a Second-Order Adaptive Sliding-Mode Observer Control Algorithm for the Sensorless Permanent Magnet Synchronous Motor. (2023). LAPSE:2023.36180
Author Affiliations
Yao G: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650504, China
Cheng Y: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650504, China
Wang Z: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650504, China
Xiao Y: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650504, China
Cheng Y: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650504, China
Wang Z: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650504, China
Xiao Y: Faculty of Transportation Engineering, Kunming University of Science and Technology, Kunming 650504, China
Journal Name
Processes
Volume
11
Issue
6
First Page
1636
Year
2023
Publication Date
2023-05-26
ISSN
2227-9717
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PII: pr11061636, Publication Type: Journal Article
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LAPSE:2023.36180
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https://doi.org/10.3390/pr11061636
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[v1] (Original Submission)
Jul 4, 2023
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Calvin Tsay
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