Scholarlink

<p>Speed control of Permanent Magnet Synchronous Motors (PMSM) is especially problematic because model uncertainty can make the effectiveness of Model Predictive Control (MPC) useless and even put on hold optimization procedures in some cases. The paper aims to develop a robust L2-gain MPC controller that employs recursive feasibility to facilitate enhanced performance in PMSM speed control with uncertainty. We developed an L2-gain MPC controller minimizing the influence of model uncertainties. To ensure recursive feasibility, we employed techniques that guarantee the optimization problem has a solution at every iteration of the MPC algorithm. Experimental experiments on a PMSM motor demonstrate that the proposed method is effective in ensuring continuous operation of the optimization process regardless of uncertainties, and solutions are always prepared. The L2-gain MPC controller presented in this paper offers an efficient solution for PMSM speed control with robustness guarantees and performance improvement against model uncertainties. The results of practical tests showed that in the steady state, the proposed method has an error of 1&#xa0;rpm, and in the transient state and for ramp inputs, in the worst case, it has an error of 2&#xa0;rpm.</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

A robust recursive feasibility-based MPC method for the speed control of uncertain permanent magnet synchronous motors

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月16日

查看全文
  • Yuquan Zhou

摘要

Speed control of Permanent Magnet Synchronous Motors (PMSM) is especially problematic because model uncertainty can make the effectiveness of Model Predictive Control (MPC) useless and even put on hold optimization procedures in some cases. The paper aims to develop a robust L2-gain MPC controller that employs recursive feasibility to facilitate enhanced performance in PMSM speed control with uncertainty. We developed an L2-gain MPC controller minimizing the influence of model uncertainties. To ensure recursive feasibility, we employed techniques that guarantee the optimization problem has a solution at every iteration of the MPC algorithm. Experimental experiments on a PMSM motor demonstrate that the proposed method is effective in ensuring continuous operation of the optimization process regardless of uncertainties, and solutions are always prepared. The L2-gain MPC controller presented in this paper offers an efficient solution for PMSM speed control with robustness guarantees and performance improvement against model uncertainties. The results of practical tests showed that in the steady state, the proposed method has an error of 1 rpm, and in the transient state and for ramp inputs, in the worst case, it has an error of 2 rpm.