Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (24): 233-236.DOI: 10.3778/j.issn.1002-8331.2010.24.068

• 工程与应用 • Previous Articles     Next Articles

Study of higher-order iterative learning control on nonlinear sampled system

XU Hong-wei,HUANG Yan-yan,SUN Jian   

  1. College of Mechanical Engineering,China Jiliang University,Hangzhou 310018,China
  • Received:2009-03-16 Revised:2009-05-21 Online:2010-08-21 Published:2010-08-21
  • Contact: XU Hong-wei

一类非线性采样系统高阶迭代学习控制

徐红伟,黄艳岩,孙 坚   

  1. 中国计量学院 机电工程学院,杭州 310018
  • 通讯作者: 徐红伟

Abstract: A higher-order iterative learning control law is presented on a nonlinear sampled system with relative degree and time delays.It is proved that the iterative learning control converges to a desired control if the output and the vector fields of the control system satisfy the Lipschiz conditions and the sampling period is short enough.A comparison is made between the convergence of a nonlinear system with higher-order relative degree by simulating.The result shows that the system with higher-order relative degree?has a larger convergence rate.

摘要: 迭代学习控制能够实现期望轨迹的完全跟踪而被广泛关注,但是采样迭代学习控制成果目前还比较少。针对一类有相对阶和输出延迟的非线性采样系统,研究了高阶迭代学习控制算法。利用Newton-Leibniz公式、贝尔曼引理和Lipschiz条件证明了当系统的采样周期足够小,迭代学习初态严格重复,且学习增益满足要求的条件,那么系统输出在采样点上收敛于期望输出。对一阶和二阶学习算法的仿真表明高阶算法在收敛速度上比一阶有明显改善。

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