Improved Super-Twisting Sliding Mode Control With a Novel Extended State Disturbance Observer for PMSM Drives

For the sake of solving the undesirable chattering in the conventional super-twisting sliding mode controller (STSMC), this paper proposes an improved STSMC and a novel extended state observer (ESO) for the drives of permanent magnet synchronous motors (PMSMs). First, an elliptic saturation function is designed to construct the improved STSMC and its finite-time convergence is analyzed. Therefore, a novel ESO is introduced to diminish the switching gain and compensate the lumped disturbance of the PMSM system. Ultimately, the results of simulation demonstrate that the proposed method effectively reduces chattering while enhancing dynamic response and robustness.

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Published
2024-11-29
DOI
https://doi.org/10.1109/irac63143.2024.10871818
Citations
1
Primary Topic
Iterative Learning Control Systems
Type
article
Field-Weighted Citation Impact
0.33
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article

Improved Super-Twisting Sliding Mode Control With a Novel Extended State Disturbance Observer for PMSM Drives

Shu Min Tao, Meng Li, Zhonghua Wang, Dongxue Wang
1 citations
Iterative Learning Control Systems
0.33
article

Improved Super-Twisting Sliding Mode Control With a Novel Extended State Disturbance Observer for PMSM Drives

Shu Min Tao, Meng Li, Zhonghua Wang, Dongxue Wang
article en
1 citations

Abstract

For the sake of solving the undesirable chattering in the conventional super-twisting sliding mode controller (STSMC), this paper proposes an improved STSMC and a novel extended state observer (ESO) for the drives of permanent magnet synchronous motors (PMSMs). First, an elliptic saturation function is designed to construct the improved STSMC and its finite-time convergence is analyzed. Therefore, a novel ESO is introduced to diminish the switching gain and compensate the lumped disturbance of the PMSM system. Ultimately, the results of simulation demonstrate that the proposed method effectively reduces chattering while enhancing dynamic response and robustness.

University of Jinan (CN)
Climate action
Openalex Percentile: Top 37%
Iterative Learning Control Systems
0.33
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