Data-Based Iterative Learning Control: A Nonconservative Approach via LMI Techniques

被引:0
|
作者
Wang, Chenchao [1 ,2 ]
Meng, Deyuan [1 ,2 ]
Cheng, Long [3 ]
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
[3] Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
来源
2024 IEEE 18TH INTERNATIONAL CONFERENCE ON CONTROL & AUTOMATION, ICCA 2024 | 2024年
基金
中国国家自然科学基金;
关键词
D O I
10.1109/ICCA62789.2024.10591942
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 aims to propose a data-based iterative learning control (ILC) framework that addresses the tracking issues without imposing additional assumptions on the sufficiency of sampled data. By introducing the concepts of k-state system and robust k-stability, we establish a connection between time-domain tracking issues and iteration-domain k-stabilization. Moreover, with the application of some helpful linear matrix inequality (LMI) techniques, we convert the data-based ILC synthesis into solving equivalent LMI conditions. As a result, the tracking error is recursively corrected and satisfied tracking performances are achieved by leveraging as few sampled data as possible. To demonstrate the effectiveness of the proposed ILC framework, illustrative simulations on an injection molding process are also provided.
引用
收藏
页码:653 / 658
页数:6
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