Scholarlink

<p>Permanent magnet synchronous motor is a high-order nonlinear and uncertain system. Aiming at the problem that its conventional feedback control is vulnerable to various disturbance factors of the system, resulting in insufficient anti-disturbance performance, based on the mathematical model of the permanent magnet synchronous motor and the basic principle of the model reference adaptive algorithm, this paper designs a double closed-loop adaptive controller by using the generalized output error signal of the system combined with the input quantity of the forward path and the output feedback quantity of the controlled object, and utilizes the improved sine–cosine optimization algorithm to optimize the variable parameters in the controller. Finally, an experimental platform is built, and the classic PI control and the traditional model reference adaptive control are selected as the comparison groups. The experimental results verify that the fluctuation generated by the control system when disturbed is smaller, the speed of restoring stability is faster, and the anti-disturbance ability is stronger.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Double closed-loop control of PMSM based on improved model reference adaptive strategy and sine–cosine algorithm

  • 期刊论文

  • 发表日期：

    2025年4月26日

查看全文
  • Chengyang Feng,
  • Hengzhan Yang,
  • Bo Tan,
  • Fucai Qian

摘要

Permanent magnet synchronous motor is a high-order nonlinear and uncertain system. Aiming at the problem that its conventional feedback control is vulnerable to various disturbance factors of the system, resulting in insufficient anti-disturbance performance, based on the mathematical model of the permanent magnet synchronous motor and the basic principle of the model reference adaptive algorithm, this paper designs a double closed-loop adaptive controller by using the generalized output error signal of the system combined with the input quantity of the forward path and the output feedback quantity of the controlled object, and utilizes the improved sine–cosine optimization algorithm to optimize the variable parameters in the controller. Finally, an experimental platform is built, and the classic PI control and the traditional model reference adaptive control are selected as the comparison groups. The experimental results verify that the fluctuation generated by the control system when disturbed is smaller, the speed of restoring stability is faster, and the anti-disturbance ability is stronger.