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Integrated Predictive Control of PMLSM Current and Velocity Based on ST-SMO

Author

Listed:
  • Shenhui Du

    (School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
    These authors contributed equally to this work.)

  • Zihao Zhang

    (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)

  • Kangtao Wang

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

  • Hui Zhao

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

  • Zheng Li

    (School of Electrical Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
    These authors contributed equally to this work.)

Abstract

To enhance the control performance of a permanent magnet linear synchronous motor (PMLSM) and to improve its dynamic response performance and steady-state accuracy, a PMLSM model predictive integrated control (MPC) system based on a super-twisting sliding mode observer (ST-SMO) is proposed. According to the mathematical model of a PMLSM, this paper designs a three-step model to predict the comprehensive control correction factor, optimize the prediction speed and current, reduce the response time, and enhance the system’s stability. Meanwhile, in order to solve the problem of the PMLSM’s high dependence on mechanical sensors, the ST-SMO is introduced to observe the rotation speed of PMLSM, which has better tracking performance and observation accuracy than a traditional sliding mode observer (SMO). Finally, the experimental verification is carried out on the PMLSM experimental platform. The software simulation and hardware experiment results show that the control system designed in this paper not only simplifies the overall structure of the system, but it also has better control performance and tracking ability. Compared with traditional control methods and SMO, it has better control performance, stability, and speed-tracking performance.

Suggested Citation

  • Shenhui Du & Zihao Zhang & Jinsong Wang & Kangtao Wang & Hui Zhao & Zheng Li, 2022. "Integrated Predictive Control of PMLSM Current and Velocity Based on ST-SMO," Energies, MDPI, vol. 15(15), pages 1-21, July.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:15:p:5504-:d:875378
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