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This paper presents a variable universe fuzzy iterative learning control (VUFILC) strategy for the position control of permanent magnet linear synchronous motors (PMLSM), based on the sand cat swarm optimization (SCSO) algorithm. This strategy integrates the advantages of fuzzy logic, iterative learning, and swarm intelligence optimization techniques, enhancing the tracking accuracy and convergence speed of the PMLSM control system. The VUFILC enhances the adaptability and robustness of control systems by adjusting the universe of discourse online through scaling factors, which are determined by a secondary fuzzy controller. The initialization population distribution of SCSO is improved using a multi-chaotic mapping strategy, and SCSO is utilized to optimize the gain of iterative learning. The simulation results show that compared to traditional iterative learning control and fuzzy iterative learning control, the control strategy designed in this paper has better performance in reducing position tracking error and improving the convergence speed of the control system.

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Research on a PMLSM Control Strategy Based on Variable Universe Fuzzy Iterative Learning and Sand Cat Swarm Optimization

  • 会议论文

  • 发表日期：

    2025年

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  • Tao Zhang,
  • Yiqiang Zhang,
  • Yong Li,
  • Hualiang Zhang,
  • Guopeng Zhang,
  • Hao Wang

摘要

This paper presents a variable universe fuzzy iterative learning control (VUFILC) strategy for the position control of permanent magnet linear synchronous motors (PMLSM), based on the sand cat swarm optimization (SCSO) algorithm. This strategy integrates the advantages of fuzzy logic, iterative learning, and swarm intelligence optimization techniques, enhancing the tracking accuracy and convergence speed of the PMLSM control system. The VUFILC enhances the adaptability and robustness of control systems by adjusting the universe of discourse online through scaling factors, which are determined by a secondary fuzzy controller. The initialization population distribution of SCSO is improved using a multi-chaotic mapping strategy, and SCSO is utilized to optimize the gain of iterative learning. The simulation results show that compared to traditional iterative learning control and fuzzy iterative learning control, the control strategy designed in this paper has better performance in reducing position tracking error and improving the convergence speed of the control system.