Impact of Control Parameter Deviations on PMSM Operating Efficiency and Adaptive Regulation Strategy
DOI:
https://doi.org/10.5281/zenodo.20605207Keywords:
Permanent Magnet Synchronous Motor, Control Parameter Deviation, Operating Efficiency, Vector Control, Online Parameter Identification, Adaptive RegulationAbstract
Permanent magnet synchronous motors (PMSMs) are widely used in industrial automation, transportation, and intelligent equipment due to their high efficiency, high power density, and good dynamic performance. However, parameter deviations caused by temperature variation, load fluctuation, model uncertainty, and aging may reduce control accuracy and operating efficiency. This paper analyzes the influence of control parameter deviations on PMSM drive efficiency using a MATLAB/Simulink-based vector control simulation platform. Speed-loop proportional gain, integral gain, stator resistance, and rotor flux-linkage estimation error are introduced as deviation variables. Their effects on efficiency, overshoot, settling time, power factor, stator current, and copper loss are quantitatively evaluated. Based on the results, an adaptive regulation strategy combining online parameter identification and operating-condition recognition is proposed. Simulation results show that rotor flux-linkage estimation error has the strongest impact on efficiency, followed by speed-loop proportional gain deviation. The proposed strategy reduces efficiency loss by more than 60% and decreases transient extra energy consumption by approximately 58%, providing a practical reference for improving PMSM drive efficiency.
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Competing Interests Statement
The author declares that there are no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Ethical Approval Consent
This study does not involve human participants, animal experiments, or personally identifiable experimental data. Ethical approval and informed consent are therefore not applicable.
Data Availability Statement
All data generated or analyzed during this study are included in this article. The simulation data are available from the author upon reasonable request.
Declaration of Generative AI in Scientific Writing
During the preparation of this work, the author used ChatGPT for language polishing, grammar checking, formatting assistance, and manuscript organization. After using this tool, the author reviewed and edited the content as needed and takes full responsibility for the content of the publication.
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This research received no external funding.
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Copyright (c) 2026 Zhuopeng Gao (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
