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Distributed Event-triggered Bipartite Consensus for Multi-agent Systems Against Injection Attacks

Zhao, Huarong; Shan, Jinjun; Peng, Li; Yu, Hongnian


Huarong Zhao

Jinjun Shan

Li Peng


This paper studies fully distributed data-driven problems for nonlinear discrete-time multi-agent systems (MASs) with fixed and switching topologies preventing injection attacks. We first develop an enhanced compact form dynamic linearization model by applying the designed distributed bipartite combined measurement error function of the MASs. Then, a fully distributed event-triggered bipartite consensus (DETBC) framework is designed, where the dynamics information of MASs is no longer needed. Meanwhile, the restriction of the topology of the proposed DETBC method is further relieved. To prevent the MASs from injection attacks, neural network-based detection and compensation schemes are developed. Rigorous convergence proof is presented that the bipartite consensus error is ultimately boundedness. Finally, the effectiveness of the designed method is verified through simulations and experiments


Zhao, H., Shan, J., Peng, L., & Yu, H. (in press). Distributed Event-triggered Bipartite Consensus for Multi-agent Systems Against Injection Attacks. IEEE Transactions on Industrial Informatics,

Journal Article Type Article
Acceptance Date Mar 2, 2022
Online Publication Date Mar 8, 2022
Deposit Date Jun 15, 2022
Publicly Available Date Jun 16, 2022
Journal IEEE Transactions on Industrial Informatics
Print ISSN 1551-3203
Electronic ISSN 1941-0050
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Keywords Electrical and Electronic Engineering; Computer Science Applications; Information Systems; Control and Systems Engineering
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Distributed Event-triggered Bipartite Consensus For Multi-agent Systems Against Injection Attacks (1.2 Mb)

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