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Design of a Deflection Switched Reluctance Motor Control System Based on a Flexible Neural Network

Author

Listed:
  • Zheng Li

    (School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
    School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

  • Xiaopeng Wei

    (School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China)

  • Jinsong Wang

    (School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China)

  • Libo Liu

    (School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China)

  • Shenhui Du

    (School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China)

  • Xiaoqiang Guo

    (School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China)

  • Hexu Sun

    (School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China)

Abstract

Deflection switched reluctance motors (DSRM) are prone to chattering at low speeds, which always affects the output efficiency of the DSRM and the mechanical loss of the motor. Combining the characteristics of a traditional reluctance motor with the strong nonlinear and high coupling of the DSRM, a control system for a DSRM based on a flexible neural network (FNN) is proposed in this paper. Based on the better robustness and fault tolerance of fuzzy PI control, the given speed signal is adjusted and converted into a torque control signal. As a result, the FNN control module possesses the strong self-learning ability and adaptive adjustment ability necessary to obtain the control voltage signal. Through simulations and experiments, it was verified that the control system can run stably on DSRM and shows good dynamic performance and anti-interference ability.

Suggested Citation

  • Zheng Li & Xiaopeng Wei & Jinsong Wang & Libo Liu & Shenhui Du & Xiaoqiang Guo & Hexu Sun, 2022. "Design of a Deflection Switched Reluctance Motor Control System Based on a Flexible Neural Network," Energies, MDPI, vol. 15(11), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:4172-:d:832612
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    References listed on IDEAS

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    1. Krzysztof Tomczewski & Krzysztof Wrobel & Daniel Rataj & Grzegorz Trzmiel, 2021. "A Switched Reluctance Motor Drive Controller Based on an FPGA Device with a Complex PID Regulator," Energies, MDPI, vol. 14(5), pages 1-22, March.
    2. Md Sydur Rahman & Grace Firsta Lukman & Pham Trung Hieu & Kwang-Il Jeong & Jin-Woo Ahn, 2021. "Optimization and Characteristics Analysis of High Torque Density 12/8 Switched Reluctance Motor Using Metaheuristic Gray Wolf Optimization Algorithm," Energies, MDPI, vol. 14(7), pages 1-17, April.
    3. Piotr Bogusz & Mariusz Korkosz & Jan Prokop & Mateusz Daraż, 2021. "Analysis Performance of SRM Based on the Novel Dependent Torque Control Method," Energies, MDPI, vol. 14(24), pages 1-18, December.
    4. Mahmoud A. Gaafar & Arwa Abdelmaksoud & Mohamed Orabi & Hao Chen & Mostafa Dardeer, 2021. "Performance Investigation of Switched Reluctance Motor Driven by Quasi-Z-Source Integrated Multiport Converter with Different Switching Algorithms," Sustainability, MDPI, vol. 13(17), pages 1-14, August.
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    Cited by:

    1. Yun Zhang & Liang Chen & Zhixue Wang & Enguang Hou, 2022. "Speed Control of Switched Reluctance Motor Based on Regulation Region of Switching Angle," Energies, MDPI, vol. 15(16), pages 1-24, August.

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