重庆理工大学学报(自然科学) ›› 2023, Vol. 37 ›› Issue (5): 29-36.

• 车辆工程 • 上一篇    下一篇

采用自适应遗忘因子的永磁同步电机预测电流控制

龙 涛,刘蕴博,常九健   

  1. (1.合肥工业大学 汽车与交通工程学院,合肥 230009; 2.合肥工业大学 汽车工程技术研究院,合肥 230009)
  • 出版日期:2023-06-21 发布日期:2023-06-21
  • 作者简介:龙涛,男,硕士,主要从事电机控制研究,Email:847801171@qq.com;刘蕴博,男,博士,研究员,主要从事整车轻 量化研究,Email:2017800535@hfut.edu.cn。

Predictive current control of permanent magnet synchronous motors based on adaptive forgetting factors

  • Online:2023-06-21 Published:2023-06-21

摘要: 由于模型预测控制(modelpredictivecontrol,MPC)是基于电机模型实现预测控制 的,电机实际参数与预测模型参数的不匹配会导致控制系统的控制效果下降。针对此问题提出 一种采用自适应遗忘因子的最小二乘法参数辨识,该方法通过变化的遗忘因子调节辨识过程中 旧数据的遗忘程度,使辨识结果具有快速的收敛性且能够稳定地跟随电机参数变化。通过辨识 电机参数对预测模型的参数进行实时修正,可以有效降低因电机参数变化而导致的电流和转矩 的波动,提高 MPC算法的控制性能,提升 MPC的参数鲁棒性。最后用 Matlab/Simulink进行仿 真分析,验证了该方法的有效性。

关键词: 模型预测控制, 参数辨识, 最小二乘法, 自适应遗忘因子, 参数鲁棒性

Abstract: Since model predictive control (MPC) works based on a motor model, the mismatch between the actual parameters of the motor and the parameters of the predicted model will lead to a decrease in the control performance of the system. Aiming at this problem, this paper proposes a least square parameter identification method by using adaptive forgetting factors. This method adjusts the forgetting degree of the old data in the identification process through the changing forgetting factors so that the identification results have fast convergence and can stably follow the changes of the motor parameters. The parameters of the prediction model are corrected in real time by identifying the motor parameters, which can effectively reduce the current and torque fluctuations caused by changes in motor parameters. The control performance and the parameter robustness of the MPC algorithm are both improved. Finally, Matlab/Simulink is used for simulation analysis, and its results verify the effectiveness of the method.

中图分类号: 

  • TM301.2