华南理工大学学报(自然科学版) ›› 2015, Vol. 43 ›› Issue (3): 103-110.doi: 10.3969/j.issn.1000-565X.2015.03.016

• 电子、通信与自动控制 • 上一篇    下一篇

基于边界层的一类不确定系统的迭代学习控制

李向阳 田森平   

  1. 华南理工大学 自动化科学与工程学院,广东 广州 510640
  • 收稿日期:2014-05-14 修回日期:2014-10-15 出版日期:2015-03-25 发布日期:2015-02-10
  • 通信作者: 李向阳(1969-),男,博士,副教授,主要从事学习控制、嵌入式系统和工业自动化研究. E-mail:xyangli@scut.edu.cn
  • 作者简介:李向阳(1969-),男,博士,副教授,主要从事学习控制、嵌入式系统和工业自动化研究.
  • 基金资助:
    国家自然科学基金资助项目(61374104);广东省自然科学基金资助项目(S2012010009675);华南理工大学中央高校科研业务费专项资金资助项目(2014ZZ0043)

Boundary Layer-Based Iterative Learning Control for a Class of Uncertain Systems

Li Xiang-yang Tian Sen-ping   

  1. School of Automation Science and Engineering,South China University of Technology,Guangzhou 510640,Guangdong,China
  • Received:2014-05-14 Revised:2014-10-15 Online:2015-03-25 Published:2015-02-10
  • Contact: 李向阳(1969-),男,博士,副教授,主要从事学习控制、嵌入式系统和工业自动化研究. E-mail:xyangli@scut.edu.cn
  • About author:李向阳(1969-),男,博士,副教授,主要从事学习控制、嵌入式系统和工业自动化研究.
  • Supported by:
    Supported by the National Natural Science Foundation of China(NSFC)(61374104) and the Natural Science Foundation of Guangdong Province of China(S2012010009675)

摘要: 针对一类具有任意初态和非周期有界扰动的不确定非线性时变系统,提出一种基于边界层的迭代学习控制方法,将边界层设计成一个具有剩余宽度的指数衰减函数,通过边界层把任意初态问题转换为零初值迭代学习问题. 针对周期和非周期不确定性扰动,分别设计周期项的学习律和非周期项的边界学习律,然后在此基础上给出了迭代学习控制算法. 文中给出了相关定理,并应用类 Lyapunov 方法给出了定理的详细证明. 仿真结果表明,所提出的算法是有效的,轨迹跟踪误差能收敛到边界层.

关键词: 不确定系统, 迭代学习控制, 初始状态问题, 边界层, 类 Lyapunov 方法

Abstract: For a class of uncertain nonlinear time-varying systems with arbitrary initial states and aperiodic bounded disturbance,an iterative learning control (ILC) method on the basis of boundary layer is presented. In this me-thod,boundary layer is designed as a decaying exponential function with residual width,the arbitrary initial state problem of ILC is transformed into a zero initial state problem by the designed boundary layer,and learning laws of periodic and aperiodic terms are designed for periodic and aperiodic disturbances,respectively. On the basis of these two laws,an ILC algorithm is presented,and the corresponding theorem is given with detail proof through Lyapunov-like approach. Simulated results show that the proposed algorithm is effective and is capable of conver-ging trajectory tracking errors to boundary layer.

Key words: uncertain system, iterative learning control, initial state problem, boundary layer, Lyapunov-like approach

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