Iterative learning control for impulsive multi-agent systems with varying trial lengths

被引：3

作者：

Cao, Xiaokai ^{[1
]}

Feckan, Michal ^{[2
,3
]}

Shen, Dong ^{[4
]}

Wang, Jin Rong ^{[1
]}

机构：

[1] Guizhou Univ, Dept Math, Guiyang 550025, Guizhou, Peoples R China

[2] Comenius Univ, Fac Math Phys & Informat, Dept Math Anal & Numer Math, Bratislava 84248, Slovakia

[3] Slovak Acad Sci, Math Inst, Stefanikova 49, Bratislava 81473, Slovakia

[4] Renmin Univ China, Sch Math, Beijing, Peoples R China

来源：

NONLINEAR ANALYSIS-MODELLING AND CONTROL | 2022年 / 27卷 / 03期

基金：

中国国家自然科学基金;

关键词：

impulsive multi-agent system; consensus tracking; fractional iterative learning control; domain alignment operator; varying trial lengths; CONSENSUS; TRACKING;

D O I：

10.15388/namc.2022.27.25475

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In this paper, we introduce iterative learning control (ILC) schemes with varying trial lengths (VTL) to control impulsive multi-agent systems (I-MAS). We use domain alignment operator to characterize each tracking error to ensure that the error can completely update the control function during each iteration. Then we analyze the system's uniform convergence to the target leader. Further, we use two local average operators to optimize the control function such that it can make full use of the iteration error. Finally, numerical examples are provided to verify the theoretical results.

引用

页码：445 / 465

页数：21

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