迭代学习控制技术回顾与长期学习控制展望
Retrospective review of some iterative learning control techniques with a comment on prospective long-term learning
摘要点击 1940  全文点击 1487  投稿时间:2012-08-11  修订日期:2012-08-28
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DOI编号  10.7641/j.issn.1000-8152.2012.8.LCTA120866
  2012,29(8):966-973
中文关键词  iterative learning control  convergence analysis  initial state shift  higher-order learning law  large-scale systems  long-term learning control
英文关键词  迭代学习控制  收敛性分析  初始状态漂移  高阶学习律  大系统  长期学习控制
基金项目  This work was supported by the National Natural Science Foundation of China (No. F030114-60974140), and the Funding of Intelligent Robot Research Center, Soongsil University, Korea.
作者单位E-mail
阮小娥 西安交通大学 数学与统计学院 wruanxe@mail.xjtu.edu.cn 
朴光贤 光云大学 机器人学院  
卞增男 蔚山国立科技大学 电子与计算机工程学院  
中文摘要
      本文首先回顾了迭代学习控制中初始状态漂移问题和单调收敛性分析的研究技术. 其次, 综述了高阶迭代学习控制机制及其收敛速度比较和有效性. 再次, 评述了重复运行大系统和变幅值大工业过程的迭代学习控制机理. 最后, 展望了长期学习控制的研究趋势等.
英文摘要
      This paper firstly makes a retrospective review of some iterative learning control techniques and results regarding to the initial state shift issue and the monotone convergence analysis. Secondly, the paper presents a review of the higher-order iterative learning control scheme including its convergence speed comparison and effectiveness. Then, the paper exhibits a review of iterative learning control mechanism for large-scale systems including repetitive systems and magnitude-varying industrial processes. Lastly, the paper gives a comment on prospective long-term learning control for the future.