Iterative learning control for impulsive multi-agent systems with varying trial lengths
Articles
Xiaokai Cao
Guizhou University
https://orcid.org/0000-0002-7394-2117
Michal Fečkan
Comenius University in Bratislava
https://orcid.org/0000-0002-7385-6737
Dong Shen
Renmin University of China
JinRong Wang
Guizhou University
https://orcid.org/0000-0002-6642-1946
Published 2022-01-26
https://doi.org/10.15388/namc.2022.27.25475
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Keywords

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

How to Cite

Cao X., Fečkan M., Shen D. and Wang J. (2022) “Iterative learning control for impulsive multi-agent systems with varying trial lengths”, Nonlinear Analysis: Modelling and Control, 27(3), pp. 445-465. doi: 10.15388/namc.2022.27.25475.

Abstract

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.

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