Feedback-aided PD-type iterative learning control: initial condition problem and rectifying strategies

被引:0
|
作者
Sun, Ming-Xuan [1 ]
Bi, Hong-Bo [1 ]
Zhou, Guo-Liang [1 ]
Wang, Hui-Feng [1 ]
机构
[1] College of Information Engineering, Zhejiang University of Technology, Hangzhou,310023, China
来源
Zidonghua Xuebao/Acta Automatica Sinica | 2015年 / 41卷 / 01期
关键词
Asymptotic convergence - Convergence analysis - Desired trajectories - Finite-time control - Initial conditions - Initial rectifying - Iterative learning control - Numerical results;
D O I
10.16383/j.aas.2015.c140133
中图分类号
学科分类号
摘要
This paper addresses the problem of iterative learning control for systems in the presence of a fixed initial shift. A feedback-aided PD-type learning algorithm is proposed, and the convergence analysis indicates that such a learning algorithm can ensure that the tracking error achieves asymptotic convergence with respect to time, as the iteration approaches infinity. Furthermore, the initial rectifying and terminal converging strategies are adopted respectively to form learning algorithms for eliminating the effect of the fixed initial shift. It is shown that the system output converges to the desired trajectory over a pre-specified time interval no matter what value the fixed initial shift takes. Numerical results are presented to demonstrate the effectiveness of the proposed learning algorithms. Copyright © 2015 Acta Automatica Sinica. All rights reserved.
引用
收藏
页码:157 / 164
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