Deterministic Tracking for Continuous-Time ILC Systems With Nonrepetitive Time Intervals

被引：0
|
作者：
Zhang, Jingyao [1 ,2 ]
Meng, Deyuan [1 ,2 ]
机构：
[1] Beihang Univ BUAA, Res Div 7, Beijing 100191, Peoples R China
[2] Beihang Univ BUAA, Sch Automat Sci & Elect Engn, Beijing 100191, Peoples R China
来源：
2021 AMERICAN CONTROL CONFERENCE (ACC) | 2021年
基金：
中国国家自然科学基金;
关键词：
ITERATIVE LEARNING CONTROL; CONVERGENCE;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper is aimed at realizing robust tracking tasks for continuous-time iterative learning control (ILC) systems subject to nonrepetitive time intervals. A modified P-type ILC algorithm is proposed such that the deterministic tracking of continuous-time ILC can be ensured in the presence of the nonrepetitive time intervals. Moreover, a convergence analysis approach to continuous-time ILC is given by applying an extended contraction mapping-based method. An example is also included to verify our derived robust ILC results.
引用
收藏
页码：2218 / 2223
页数：6
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