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. |
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| 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] |
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| Database: | Engineering Source |
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| 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] |
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| ISSN: | 15618625 |
| DOI: | 10.1002/asjc.3675 |
