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Nonlinear Dynamic Model-Based Position Control Parameter Optimization Method of Planar Switched Reluctance Motors

Author

Listed:
  • Su-Dan Huang

    (Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China)

  • Zhixiang Lin

    (Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China)

  • Guang-Zhong Cao

    (Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China)

  • Ningpeng Liu

    (Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China)

  • Hongda Mou

    (Guangdong Key Laboratory of Electromagnetic Control and Intelligent Robots, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, China)

  • Junqi Xu

    (National Maglev Transportation Engineering R&D Center, Tongji University, Shanghai 201804, China)

Abstract

Currently, there are few systematic position control parameter optimization methods for planar switched reluctance motors (PSRMs); how to effectively optimize the control parameters of PSRMs is one of the critical issues that needs to be urgently solved. Therefore, a nonlinear dynamic model-based position control parameter optimization method of PSRMs is proposed in this paper. First, to improve the accuracy of the motor dynamics model, a Hammerstein–Wiener model based on the BP neural network input–output nonlinear module is established by combining the linear model and nonlinear model structures so that the nonlinear and linear characteristics of the system are characterized simultaneously. Then, a position control parameter optimization system of PSRMs is developed using the established Hammerstein–Wiener model. In addition, with a self-designed simulated annealing adaptive particle swarm optimization algorithm (SAAPSO), the position control parameter optimization system is performed offline iteratively to obtain the optimal position control parameters. Simulations and experiments are carried out and the corresponding results show that the optimal position control parameters obtained by the proposed method can be directly applied in the actual control system of PSRMs and the control performance is improved effectively using the obtained optimal control parameters.

Suggested Citation

  • Su-Dan Huang & Zhixiang Lin & Guang-Zhong Cao & Ningpeng Liu & Hongda Mou & Junqi Xu, 2023. "Nonlinear Dynamic Model-Based Position Control Parameter Optimization Method of Planar Switched Reluctance Motors," Mathematics, MDPI, vol. 11(19), pages 1-19, September.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:19:p:4067-:d:1247369
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    References listed on IDEAS

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    1. J. Zambrano & J. Sanchis & J. M. Herrero & M. Martínez, 2020. "A Unified Approach for the Identification of Wiener, Hammerstein, and Wiener–Hammerstein Models by Using WH-EA and Multistep Signals," Complexity, Hindawi, vol. 2020, pages 1-23, February.
    2. Tuyen Danh Pham & Young Won Lee & Chanhum Park & Kang Ryoung Park, 2022. "Deep Learning-Based Detection of Fake Multinational Banknotes in a Cross-Dataset Environment Utilizing Smartphone Cameras for Assisting Visually Impaired Individuals," Mathematics, MDPI, vol. 10(9), pages 1-27, May.
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