Supercritical Sliding Mode Control for Position Tracking of PMSM With Disturbance Rejection

In highly dynamic environments, such as industrial automation systems with frequent load changes or electric vehicles, ensuring both robustness and high-precision control remains challenging, particularly in the position control of permanent magnet synchronous motors. Sliding-mode control (SMC) is widely used for its robustness, but traditional SMC methods often suffer from chattering and slow convergence, limiting overall system performance. To address these limitations, this study proposes a robust SMC approach incorporating a novel nonlinear sliding surface inspired by the supercritical pitchfork bifurcation concept. Additionally, a new reaching law is introduced to attenuate chattering and ensure fast convergence of the sliding variable to zero within a finite time. The proposed method also integrates a proportional-integral observer (PIO) with the equivalent-input disturbance (EID) framework to enhance disturbance rejection under both matched and mismatched uncertainties. The stability of the proposed controller was verified using Lyapunov stability analysis, and its performance was validated through simulations and real-world experiments. Comparative evaluations demonstrated that the proposed reaching law significantly reduces chattering amplitude by over 50 % and improves convergence speed by approximately 5.6 times faster than conventional methods. Furthermore, the system's disturbance-rejection capability under matched and mismatched uncertainties is enhanced through the PIO+EID structure. These results confirmed the robust performance of the controller under unknown disturbances. This paper is accompanied by a video that demonstrates the proposed control strategy and provides a comparison with traditional controllers.

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Publication Details

Journal
IEEE Transactions on Power Electronics
Published
2025-08-28
DOI
https://doi.org/10.1109/tpel.2025.3603876
Citations
4
Primary Topic
Iterative Learning Control Systems
Type
article
Field-Weighted Citation Impact
4.86
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article

Supercritical Sliding Mode Control for Position Tracking of PMSM With Disturbance Rejection

Sunghyun Choi, Dongwon Yun, Jisu Kim
4 citations
IEEE Transactions on Power Electronics
Iterative Learning Control Systems
4.86
article

Supercritical Sliding Mode Control for Position Tracking of PMSM With Disturbance Rejection

Sunghyun Choi, Dongwon Yun, Jisu Kim
article en
4 citations

Abstract

In highly dynamic environments, such as industrial automation systems with frequent load changes or electric vehicles, ensuring both robustness and high-precision control remains challenging, particularly in the position control of permanent magnet synchronous motors. Sliding-mode control (SMC) is widely used for its robustness, but traditional SMC methods often suffer from chattering and slow convergence, limiting overall system performance. To address these limitations, this study proposes a robust SMC approach incorporating a novel nonlinear sliding surface inspired by the supercritical pitchfork bifurcation concept. Additionally, a new reaching law is introduced to attenuate chattering and ensure fast convergence of the sliding variable to zero within a finite time. The proposed method also integrates a proportional-integral observer (PIO) with the equivalent-input disturbance (EID) framework to enhance disturbance rejection under both matched and mismatched uncertainties. The stability of the proposed controller was verified using Lyapunov stability analysis, and its performance was validated through simulations and real-world experiments. Comparative evaluations demonstrated that the proposed reaching law significantly reduces chattering amplitude by over 50 % and improves convergence speed by approximately 5.6 times faster than conventional methods. Furthermore, the system's disturbance-rejection capability under matched and mismatched uncertainties is enhanced through the PIO+EID structure. These results confirmed the robust performance of the controller under unknown disturbances. This paper is accompanied by a video that demonstrates the proposed control strategy and provides a comparison with traditional controllers.

IEEE Transactions on Power ElectronicsVol. 41(1)
Daegu Gyeongbuk Institute of Science and Technology (KR), Korea Institute of Industrial Technology (KR)
Openalex Percentile: Top 5%
Iterative Learning Control Systems
4.86
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