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Predictive Variable Gain Iterative Learning Control for PMSM

机译：PMSM的预测变量增益迭代学习控制

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摘要

A predictive variable gain strategy in iterative learning control (ILC) is introduced. Predictive variable gain iterative learning control is constructed to improve the performance of trajectory tracking. A scheme based on predictive variable gain iterative learning control for eliminating undesirable vibrations of PMSM system is proposed. The basic idea is that undesirable vibrations of PMSM system are eliminated from two aspects of iterative domain and time domain. The predictive method is utilized to determine the learning gain in the ILC algorithm. Compression mapping principle is used to prove the convergence of the algorithm. Simulation results demonstrate that the predictive variable gain is superior to constant gain and other variable gains.
机译：介绍了一种迭代学习控制（ILC）中的预测变量增益策略。构建了预测变量增益迭代学习控制，以提高轨迹跟踪的性能。提出了一种基于预测变量增益迭代学习控制的消除永磁同步电机系统不良振动的方案。基本思想是从迭代域和时域两个方面消除PMSM系统的不希望有的振动。预测方法用于确定ILC算法中的学习增益。利用压缩映射原理证明了算法的收敛性。仿真结果表明，预测可变增益优于恒定增益和其他可变增益。

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  • 来源
    《Journal of control science and engineering》 |2015年第2015期|353712.1-353712.6|共6页
  • 作者

    Huimin Xu; Xuedong Zhang; Xiangjie Liu;

  • 作者单位

    The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China;

    School of Energy and Power Engineering, North China Electric Power University, Baoding 071003, China;

    The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China;

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