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PMSM Vector Control Strategy Based on Active Disturbance Rejection Controller

Author

Listed:
  • Kai Zhou

    (Engineering Research Center of Automotive Electronics Drive Control and System Integration, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China)

  • Min Ai

    (Engineering Research Center of Automotive Electronics Drive Control and System Integration, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China)

  • Yancheng Sun

    (State Grid Beijing Electric Power Company, Beijing 100000, China)

  • Xiaogang Wu

    (Engineering Research Center of Automotive Electronics Drive Control and System Integration, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China)

  • Ran Li

    (Engineering Research Center of Automotive Electronics Drive Control and System Integration, Ministry of Education, Harbin University of Science and Technology, Harbin 150080, China)

Abstract

Based on current research into the vector control principles of the permanent magnet synchronous motor (PMSM), a control strategy founded upon an Active Disturbances Rejection Controller (ADRC) is proposed. This control strategy consists of an ADRC speed loop and current controller. By studying the factors affecting the running state of a PMSM, a mathematical model is established, and the design principle of the active disturbances rejection controller is analyzed in order to design the ADRC speed loop. The speed loop considers errors caused by uncertain factors, such as external disturbances, to be the disturbance amount, which is observed and then compensated for by the ADRC, thereby improving the dynamic and static performance as well as the anti-disturbance capability of the system. In order to achieve the strong coupling of the PMSM, the current controller was also designed to decouple the d–q axis current. Our simulation and experimental results demonstrate the feasibility and practicability of this control strategy.

Suggested Citation

  • Kai Zhou & Min Ai & Yancheng Sun & Xiaogang Wu & Ran Li, 2019. "PMSM Vector Control Strategy Based on Active Disturbance Rejection Controller," Energies, MDPI, vol. 12(20), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:20:p:3827-:d:274899
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    References listed on IDEAS

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    1. Yangwu Shen & Mingjian Cui & Qin Wang & Feifan Shen & Bin Zhang & Liqing Liang, 2017. "Comprehensive Reactive Power Support of DFIG Adapted to Different Depth of Voltage Sags," Energies, MDPI, vol. 10(6), pages 1-20, June.
    2. Mingcheng Lyu & Gongping Wu & Derong Luo & Fei Rong & Shoudao Huang, 2019. "Robust Nonlinear Predictive Current Control Techniques for PMSM," Energies, MDPI, vol. 12(3), pages 1-19, January.
    3. Dandan Su & Chengning Zhang & Yugang Dong, 2017. "An Improved Continuous-Time Model Predictive Control of Permanent Magnetic Synchronous Motors for a Wide-Speed Range," Energies, MDPI, vol. 10(12), pages 1-18, December.
    4. Zifa Liu & Wenhua Zhang & Changhong Zhao & Jiahai Yuan, 2015. "The Economics of Wind Power in China and Policy Implications," Energies, MDPI, vol. 8(2), pages 1-18, February.
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    Cited by:

    1. Mingfei Huang & Yongting Deng & Hongwen Li & Meng Shao & Jing Liu, 2021. "Integrated Uncertainty/Disturbance Suppression Based on Improved Adaptive Sliding Mode Controller for PMSM Drives," Energies, MDPI, vol. 14(20), pages 1-19, October.
    2. Pingyue Zhang & Jingyu Zhang & Yingshun Li & Yuhu Wu, 2020. "Nonlinear Active Disturbance Rejection Control of VGT-EGR System in Diesel Engines," Energies, MDPI, vol. 13(20), pages 1-20, October.
    3. Kai Zhou & Min Ai & Dongyang Sun & Ningzhi Jin & Xiaogang Wu, 2019. "Field Weakening Operation Control Strategies of PMSM Based on Feedback Linearization," Energies, MDPI, vol. 12(23), pages 1-18, November.
    4. Feng Jiang & Fan Yang & Songjun Sun & Kai Yang, 2022. "Improved Linear Active Disturbance Rejection Control for IPMSM Drives Considering Load Inertia Mismatch," Energies, MDPI, vol. 15(3), pages 1-22, February.
    5. Marcel Nicola & Claudiu-Ionel Nicola, 2022. "Improvement of Linear and Nonlinear Control for PMSM Using Computational Intelligence and Reinforcement Learning," Mathematics, MDPI, vol. 10(24), pages 1-34, December.

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