Sampled-data Iterative Learning Control for Nonlinear Systems with Iteration Varying Lengths

被引：0

作者：

Wang, Lan-Jing ^{[1
]}

Shen, Dong ^{[1
]}

Li, Xuefang ^{[2
]}

Chien, Chiang-Ju ^{[3
]}

Wang, Ying-Chung ^{[3
]}

机构：

[1] Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China

[2] Imperial Coll London, Dept Elect & Elect Engn, South Kensington Campus, London SW7 2AZ, England

[3] Huafan Univ, Dept Elect Engn, New Taipei, Taiwan

来源：

2017 6TH DATA DRIVEN CONTROL AND LEARNING SYSTEMS (DDCLS) | 2017年

基金：

中国国家自然科学基金; 北京市自然科学基金;

关键词：

Iterative Learning Control; Sampled-Data; Nonlinear Systems; Iteration Varying Lengths; REPETITIVE CONTROL;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This note addresses the problem of sampled-data iterative learning control (SDILC) for continuous-time nonlinear systems with randomly iteration varying lengths. To deal with the iteration varying trial lengths, a P-type ILC scheme with a modified tracking error is proposed. Sufficient conditions are derived to ensure the convergence of the nonlinear system at each sampling instant. An illustrative example is carried out to verify the effectiveness of the proposed ILC algorithm.

引用

页码：36 / 41

页数：6

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