{"title":"Event-triggered iterative learning formation control for a class of nonlinear multi-agent systems under deception attack","authors":"Xuhui Bu, Wenjing Ma, Yanling Yin","doi":"10.1002/asjc.3675","DOIUrl":null,"url":null,"abstract":"<p>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.</p>","PeriodicalId":55453,"journal":{"name":"Asian Journal of Control","volume":"28 2","pages":"659-671"},"PeriodicalIF":2.7000,"publicationDate":"2026-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Asian Journal of Control","FirstCategoryId":"94","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/asjc.3675","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/5/5 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍：
The Asian Journal of Control, an Asian Control Association (ACA) and Chinese Automatic Control Society (CACS) affiliated journal, is the first international journal originating from the Asia Pacific region. The Asian Journal of Control publishes papers on original theoretical and practical research and developments in the areas of control, involving all facets of control theory and its application.
Published six times a year, the Journal aims to be a key platform for control communities throughout the world.
The Journal provides a forum where control researchers and practitioners can exchange knowledge and experiences on the latest advances in the control areas, and plays an educational role for students and experienced researchers in other disciplines interested in this continually growing field. The scope of the journal is extensive.
Topics include:
The theory and design of control systems and components, encompassing:
Robust and distributed control using geometric, optimal, stochastic and nonlinear methods
Game theory and state estimation
Adaptive control, including neural networks, learning, parameter estimation
and system fault detection
Artificial intelligence, fuzzy and expert systems
Hierarchical and man-machine systems
All parts of systems engineering which consider the reliability of components and systems
Emerging application areas, such as:
Robotics
Mechatronics
Computers for computer-aided design, manufacturing, and control of
various industrial processes
Space vehicles and aircraft, ships, and traffic
Biomedical systems
National economies
Power systems
Agriculture
Natural resources.
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