Iterative Learning Control of Constrained Systems With Varying Trial Lengths Under Alignment Condition

被引：22

作者：

Shen, Mouquan ^{[1
]}

Wu, Xingzheng ^{[1
]}

Park, Ju H. ^{[2
]}

Yi, Yang ^{[3
]}

Sun, Yonghui ^{[4
]}

机构：

[1] Nanjing Technol Univ, Coll Elect Engn & Control Sci, Nanjing 211816, Peoples R China

[2] Yeungnam Univ, Dept Elect Engn, Gyongsan 38541, South Korea

[3] Yangzhou Univ, Sch Informat Engn, Dept Automat, Yangzhou 225127, Jiangsu, Peoples R China

[4] Hohai Univ, Coll Energy & Elect Engn, Nanjing 211100, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2023年 / 34卷 / 09期

基金：

中国国家自然科学基金; 新加坡国家研究基金会;

关键词：

Trajectory; Convergence; Adaptive systems; Nonlinear systems; MIMO communication; Iterative learning control; System performance; Iterative learning control (ILC); nonidentical trial lengths; tracking control; NONLINEAR-SYSTEMS; ROBOT MANIPULATORS; INPUT; OPERATION; TRACKING; SCHEMES; ILC;

D O I：

10.1109/TNNLS.2021.3135504

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This brief is concerned with iterative learning control (ILC) of constrained multi-input multi-output (MIMO) nonlinear systems under the state alignment condition with varying trial lengths. A modified reference trajectory is constructed to meet the alignment condition by adjusting the reference trajectory to be spatially closed. Resorting to the barrier composite energy function (BCEF) approach, an adaptive ILC scheme is built to guarantee the bounded convergence of the resultant closed-loop system. Illustrative examples are presented to verify the validity of the proposed iteration scheme.

引用

页码：6670 / 6676

页数：7

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