Sensor Vector Control of Permanent Magnet Synchronous Motor Based on Improved Sliding Mode Observer

This paper introduces an enhanced method that uses an Extended Kalman Filter and Disturbance Observer to tackle chattering and external disturbance sensitivity in traditional sliding mode observers. It designs discrete sliding mode and disturbance observers for motor models to estimate back EMF more accurately, which improves rotor position estimation in control systems. The method uses a saturation function for sliding mode switching, simplifying parameter tuning, reducing chattering, and enhancing system robustness. Simulations show it reduces observation errors from model mismatch and disturbances, boosting system robustness and accuracy.

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Publication Details

Published
2024-12-27
DOI
https://doi.org/10.1109/ic2ecs64405.2024.10928496
Primary Topic
Iterative Learning Control Systems
Type
article
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article

Sensor Vector Control of Permanent Magnet Synchronous Motor Based on Improved Sliding Mode Observer

Xinyu Chen, Yulu Zhao, Rui Zhou, Yifei Zhu
Iterative Learning Control Systems
article

Sensor Vector Control of Permanent Magnet Synchronous Motor Based on Improved Sliding Mode Observer

Xinyu Chen, Yulu Zhao, Rui Zhou, Yifei Zhu
article en

Abstract

This paper introduces an enhanced method that uses an Extended Kalman Filter and Disturbance Observer to tackle chattering and external disturbance sensitivity in traditional sliding mode observers. It designs discrete sliding mode and disturbance observers for motor models to estimate back EMF more accurately, which improves rotor position estimation in control systems. The method uses a saturation function for sliding mode switching, simplifying parameter tuning, reducing chattering, and enhancing system robustness. Simulations show it reduces observation errors from model mismatch and disturbances, boosting system robustness and accuracy.

Southwest University (CN)
Affordable and clean energy
Openalex Percentile: Top 64%
Iterative Learning Control Systems
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