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

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Title: Event‐triggered iterative learning formation control for a class of nonlinear multi‐agent systems under deception attack.
Authors: Bu, Xuhui1,2 (AUTHOR), Ma, Wenjing1 (AUTHOR), Yin, Yanling3 (AUTHOR) yyl1981hpu@126.com
Source: Asian Journal of Control. Mar2026, Vol. 28 Issue 2, p659-671. 13p.
Subjects: Iterative learning control, Multiagent systems, Binomial distribution, Control theory (Engineering), Computer simulation, Internet security, Stability theory, Discrete systems
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. [ABSTRACT FROM AUTHOR]
Copyright of Asian Journal of Control is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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An: 192204744
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  Data: Event‐triggered iterative learning formation control for a class of nonlinear multi‐agent systems under deception attack.
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  Data: <searchLink fieldCode="AR" term="%22Bu%2C+Xuhui%22">Bu, Xuhui</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Ma%2C+Wenjing%22">Ma, Wenjing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yin%2C+Yanling%22">Yin, Yanling</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> yyl1981hpu@126.com</i>
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  Data: <searchLink fieldCode="JN" term="%22Asian+Journal+of+Control%22">Asian Journal of Control</searchLink>. Mar2026, Vol. 28 Issue 2, p659-671. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Iterative+learning+control%22">Iterative learning control</searchLink><br /><searchLink fieldCode="DE" term="%22Multiagent+systems%22">Multiagent systems</searchLink><br /><searchLink fieldCode="DE" term="%22Binomial+distribution%22">Binomial distribution</searchLink><br /><searchLink fieldCode="DE" term="%22Control+theory+%28Engineering%29%22">Control theory (Engineering)</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+security%22">Internet security</searchLink><br /><searchLink fieldCode="DE" term="%22Stability+theory%22">Stability theory</searchLink><br /><searchLink fieldCode="DE" term="%22Discrete+systems%22">Discrete systems</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Asian Journal of Control is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1002/asjc.3675
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      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 13
        StartPage: 659
    Subjects:
      – SubjectFull: Iterative learning control
        Type: general
      – SubjectFull: Multiagent systems
        Type: general
      – SubjectFull: Binomial distribution
        Type: general
      – SubjectFull: Control theory (Engineering)
        Type: general
      – SubjectFull: Computer simulation
        Type: general
      – SubjectFull: Internet security
        Type: general
      – SubjectFull: Stability theory
        Type: general
      – SubjectFull: Discrete systems
        Type: general
    Titles:
      – TitleFull: Event‐triggered iterative learning formation control for a class of nonlinear multi‐agent systems under deception attack.
        Type: main
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            NameFull: Bu, Xuhui
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            NameFull: Ma, Wenjing
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          Name:
            NameFull: Yin, Yanling
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            – D: 01
              M: 03
              Text: Mar2026
              Type: published
              Y: 2026
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              Value: 28
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              Value: 2
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            – TitleFull: Asian Journal of Control
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