Event‐triggered iterative learning formation control for a class of nonlinear multi‐agent systems under deception attack

Abstract This article investigates the event‐triggered iterative learning formation control problem for a class of nonlinear multi‐agent systems under deception attack. Firstly, the deception attack existing in the transmission channel is modeled as a Bernoulli distribution with mathematical expectation, based on which the distributed formation tracking error is defined. Secondly, an event‐triggered iterative learning mechanism along the iteration axis is constructed to save communication resources. Then, utilizing the contraction mapping method and norm theory, the rigorous convergence analysis and proof are developed to confirm that the tracking error is bounded. Finally, numerical simulation is used to confirm the effectiveness of the proposed algorithm.

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Publication Details

Journal
Asian Journal of Control
Published
2025-05-05
DOI
https://doi.org/10.1002/asjc.3675
Citations
1
Primary Topic
Distributed Control Multi-Agent Systems
Type
article
Field-Weighted Citation Impact
1.49

Funders

Controls
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article

Event‐triggered iterative learning formation control for a class of nonlinear multi‐agent systems under deception attack

Yanling Yin, Xuhui Bu, Wenjing Ma
1 citations
Asian Journal of Control
Distributed Control Multi-Agent Systems
1.49
article

Event‐triggered iterative learning formation control for a class of nonlinear multi‐agent systems under deception attack

Yanling Yin, Xuhui Bu, Wenjing Ma
article en
1 citations

Abstract

Abstract This article investigates the event‐triggered iterative learning formation control problem for a class of nonlinear multi‐agent systems under deception attack. Firstly, the deception attack existing in the transmission channel is modeled as a Bernoulli distribution with mathematical expectation, based on which the distributed formation tracking error is defined. Secondly, an event‐triggered iterative learning mechanism along the iteration axis is constructed to save communication resources. Then, utilizing the contraction mapping method and norm theory, the rigorous convergence analysis and proof are developed to confirm that the tracking error is bounded. Finally, numerical simulation is used to confirm the effectiveness of the proposed algorithm.

Asian Journal of ControlVol. 28(2)
Henan Polytechnic University (CN)
National Natural Science Foundation of China, Henan Polytechnic University, Henan Provincial Science and Technology Research Project
Openalex Percentile: Top 18%
Distributed Control Multi-Agent Systems
1.49
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