A novel robust control for permanent magnet synchronous motor integrating deadbeat predictive current control and sliding mode observer

To address parameter mismatch and external load disturbance in permanent magnet synchronous motor (PMSM), a novel robust control for PMSM integrating deadbeat predictive current control (DPCC) and sliding mode observer (SMO) (NRCIDS) is proposed. Firstly, the mathematical models characterized by the PMSM dynamics and DPCC algorithm are derived. Subsequently, the robustness of DPCC against parameter variations is systematically investigated. Secondly, in the speed loop, the load disturbance is compensated by a speed loop disturbance observer. In order to reduce the chattering phenomenon of the conventional SMO, an improved exponential convergence law SMO is adopted. The dynamic response of the speed is improved by this approach, while the chattering phenomenon inherent in traditional SMO is significantly mitigated. Meanwhile, in the current loop, motor parameter disturbances are compensated by a novel terminal SMO. The proposed terminal observer suppresses chattering while it effectively reduces steady-state current error. The speed loop and current loop disturbance observers collaboratively compensate for system disturbances, thereby enhancing overall control performance. Finally, experimental results demonstrate that the proposed control strategy achieves superior performance in terms of dynamic response speed, disturbance suppression, and steady-state accuracy.

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Publication Details

Journal
Alexandria Engineering Journal
Published
2025-09-10
DOI
https://doi.org/10.1016/j.aej.2025.08.057
Citations
1
Primary Topic
Sensorless Control of Electric Motors
Type
article
Field-Weighted Citation Impact
0.73
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article

A novel robust control for permanent magnet synchronous motor integrating deadbeat predictive current control and sliding mode observer

Zhonghua Wu, Xiaozhuo Xu, Liangjie Wang, Zan Zhang
1 citations
Alexandria Engineering Journal
Sensorless Control of Electric Motors
0.73
article

A novel robust control for permanent magnet synchronous motor integrating deadbeat predictive current control and sliding mode observer

Zhonghua Wu, Xiaozhuo Xu, Liangjie Wang, Zan Zhang
article en
1 citations

Abstract

To address parameter mismatch and external load disturbance in permanent magnet synchronous motor (PMSM), a novel robust control for PMSM integrating deadbeat predictive current control (DPCC) and sliding mode observer (SMO) (NRCIDS) is proposed. Firstly, the mathematical models characterized by the PMSM dynamics and DPCC algorithm are derived. Subsequently, the robustness of DPCC against parameter variations is systematically investigated. Secondly, in the speed loop, the load disturbance is compensated by a speed loop disturbance observer. In order to reduce the chattering phenomenon of the conventional SMO, an improved exponential convergence law SMO is adopted. The dynamic response of the speed is improved by this approach, while the chattering phenomenon inherent in traditional SMO is significantly mitigated. Meanwhile, in the current loop, motor parameter disturbances are compensated by a novel terminal SMO. The proposed terminal observer suppresses chattering while it effectively reduces steady-state current error. The speed loop and current loop disturbance observers collaboratively compensate for system disturbances, thereby enhancing overall control performance. Finally, experimental results demonstrate that the proposed control strategy achieves superior performance in terms of dynamic response speed, disturbance suppression, and steady-state accuracy.

Alexandria Engineering JournalVol. 130
Jiaozuo University (CN), Université Bourgogne Franche-Comté (FR), Henan Polytechnic University (CN), Xuchang University (CN)
Openalex Percentile: Top 24%
Sensorless Control of Electric Motors
0.73
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A novel robust control for permanent magnet synchronous motor integrating deadbeat predictive current control and sliding mode observer — Zhonghua Wu, Xiaozhuo Xu, et al. · Alexandria Engineering Journal (2025) | TGRS Research Map | TGRS