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

共 50 条

[21] Two novel iterative learning control schemes for systems with randomly varying trial lengths
Li, Xuefang
Shen, Dong
SYSTEMS & CONTROL LETTERS, 2017, 107 : 9 - 16
[22] Iterative learning control for impulsive multi-agent systems with varying trial lengths
Cao, Xiaokai
Feckan, Michal
Shen, Dong
Wang, Jin Rong
NONLINEAR ANALYSIS-MODELLING AND CONTROL, 2022, 27 (03): : 445 - 465
[23] Iterative learning control for repetitive tasks with randomly varying trial lengths using successive projection
Zhuang, Zhihe
Tao, Hongfeng
Chen, Yiyang
Stojanovic, Vladimir
Paszke, Wojciech
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, 2022, 36 (05) : 1196 - 1215
[24] Adaptive Fuzzy Iterative Learning Control of Constrained Systems With Arbitrary Initial State Errors and Unknown Control Gain
Shi, Huihui
Chen, Qiang
Hong, Yihuang
Ou, Xianhua
He, Xiongxiong
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING, 2025, 22 : 6439 - 6450
[25] Adaptive Fuzzy Iterative Learning Control for High-Speed Trains With Both Randomly Varying Operation Lengths and System Constraints
Yu, Qiongxia
Hou, Zhongsheng
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2021, 29 (08) : 2408 - 2418
[26] Event-Based Switching Iterative Learning Model Predictive Control for Batch Processes With Randomly Varying Trial Lengths
Ma, Lele
Liu, Xiangjie
Gao, Furong
Lee, Kwang Y.
IEEE TRANSACTIONS ON CYBERNETICS, 2023, 53 (12) : 7881 - 7894
[27] Iterative Learning Control for discrete nonlinear systems with randomly iteration varying lengths
Shen, Dong
Zhang, Wei
Xu, Jian-Xin
SYSTEMS & CONTROL LETTERS, 2016, 96 : 81 - 87
[28] Iterative learning control for noninstantaneous impulsive fractional-order systems with varying trial lengths
Liu, Shengda
Wang, JinRong
Shen, Dong
O'Regan, D.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, 2018, 28 (18) : 6202 - 6238
[29] Constrained Iterative Learning Control for Linear Time-Varying Systems With Experimental Validation on a High-Speed Rack Feeder
Chu, Bing
Rauh, Andreas
Aschemann, Harald
Rogers, Eric
Owens, David H.
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2022, 30 (05) : 1834 - 1846
[30] An iterative learning control with alignment initial condition for a class of Nonlinear systems
Zaiyue, Yang
Chan, C. W.
PROCEEDINGS OF THE 26TH CHINESE CONTROL CONFERENCE, VOL 2, 2007, : 502 - +

← 1 2 3 4 5 →