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Volume 32Issue 1
Feb. 2023
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Article Navigation> JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY> 2023> 32(1): 107-123
Shuo Zhang, Lingding Lei, Chengning Zhang, Tian Liu, Shuli Wang. An Improved Deadbeat Predictive Current Control Method for SPMSM Drives with a Novel Adaptive Disturbance Observer[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2023, 32(1): 107-123. doi: 10.15918/j.jbit1004-0579.2022.080
Citation: Shuo Zhang, Lingding Lei, Chengning Zhang, Tian Liu, Shuli Wang. An Improved Deadbeat Predictive Current Control Method for SPMSM Drives with a Novel Adaptive Disturbance Observer[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2023, 32(1): 107-123.doi:10.15918/j.jbit1004-0579.2022.080
shu

An Improved Deadbeat Predictive Current Control Method for SPMSM Drives with a Novel Adaptive Disturbance Observer

doi:10.15918/j.jbit1004-0579.2022.080
  • 1.

    School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China

  • 2.

    School of Artificial Intelligence, Shenyang Aerospace University, Shenyang 110136, China

Funds:This work was supported by the National Natural Science Foundation of China(No. 52005037).
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  • Author Bio:

    Shuo Zhang(Member, IEEE 2018) received the B. Eng. degree from the North China Institute of Aerospace Engineering, Hebei, China, in 2011, and he received the Ph.D. degree in vehicle engineering from Beijing Institute of Technology, Beijing, China, in 2017. He is currently an Assistant Professor at National Engineering Laboratory for Electric Vehicles and School of Mechanical Engineering, Beijing Institute of Technology. His research interests include the modeling and control for the permanent magnet synchronous motor, multi-motor driving system and hybrid power system

    Lingding Leiwas born in Hunan, China, in 1999. He received the B.Eng. degree in vehicle engineering from Beijing Institute of Technology, in 2020. He is currently a M.Sc. Student at National Engineering Laboratory for Electric Vehicles and School of Mechanical Engineering, Beijing Institute of Technology. His research interests include synchronous motor drives and multi-phase motor drives

    Chengning Zhangreceived the M.E. degree in control theory and control engineering and the Ph.D. degree in vehicle engineering from Beijing Institute of Technology, Beijing, China, in 1989 and 2001, respectively. He is currently a Professor and the Vice Director of the National Engineering Laboratory for Electric Vehicles, Beijing Institute of Technology. His research interests include electric vehicles, vehicular electric motor drive systems, battery management systems, and chargers

    Tian Liuwas born in Hebei, China, in 1999. She received the B.Eng. degree in vehicle engineering from Beijing Institute of Technology, Beijing, China, in 2020. She is currently a M.Sc. Student at National Engineering Laboratory for Electric Vehicles and School of Mechanical Engineering, Beijing Institute of Technology. Her research interests include the control of permanent magnet synchronous motors and motor drive systems

    Shuli Wangwas born in Tangshan, China, in 1980. He received the master’s degree in electric machines and electric apparatus from the School of Electrical Engineering, Shenyang University of Technology, in 2009, where he is currently pursuing the Ph.D. degree. He is also an Electrical Engineer with the Liaoning General Aviation Academy. His research interest includes the modeling and optimal control of motor driving systems of electric aircraft

  • Corresponding author:m15801553369@163.com
  • Received Date:2022-07-16
  • Rev Recd Date:2022-08-12
  • Accepted Date:2022-08-15
  • Publish Date:2023-02-28
    Abstract
  • To improve the dynamic performance of conventional deadbeat predictive current control (DPCC) under parameter mismatch, especially eliminate the current overshoot and oscillation during torque mutation, it is necessary to enhance the robustness of DPCC against various working conditions. However, the disturbance from parameter mismatch can deteriorate the dynamic performance. To deal with the above problem, firstly, traditional DPCC and the parameter sensitivity of DPCC are introduced and analyzed. Secondly, an extended state observer (ESO) combined with DPCC method is proposed, which can observe and suppress the disturbance due to various parameter mismatch. Thirdly, to improve the accuracy and stability of ESO, an adaptive extended state observer (AESO) using fuzzy controller based on ESO, is presented, and combined with DPCC method. The improved DPCC-AESO can switch the value of gain coefficients with fuzzy control, accelerating the current response speed and avoid the overshoot and oscillation, which improves the robustness and stability performance of SPMSM. Finally, the three methods, as well as conventional DPCC method, DPCC-ESO method, DPCC-AESO method, are comparatively analyzed in this paper. The effectiveness of the proposed two methods are verified by simulation and experimental results.
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