Data-based adaptive predictive bipartite consensus for nonlinear multiagent systems against DoS attacks

This article investigates a Denial-of-Service (DoS) attack problem for nonlinear unknown discrete-time multiagent systems (MASs) to implement bipartite consensus tracking tasks with fixed and switching topologies. Firstly, an equivalent linearization data model of each agent is constructed using a p...

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Bibliographic Details
Published in:Proceedings of the Institution of Mechanical Engineers. Part I, Journal of systems and control engineering Journal of systems and control engineering, 2024-08, Vol.238 (7), p.1231-1241
Main Authors: Halimu, Yeerjiang, Zhao, Huarong, Yu, Hongnian, Ding, Shuchen, Qiao, Shangling
Format: Article
Language:English
Subjects:
Adaptive algorithms
Adaptive control
Adaptive systems
Compensators
Control theory
Cooperative control
Denial of service attacks
Discrete time systems
Multiagent systems
Nonlinear systems
Parameter estimation
Predictive control
Switching
Topology
Tracking errors
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Summary:This article investigates a Denial-of-Service (DoS) attack problem for nonlinear unknown discrete-time multiagent systems (MASs) to implement bipartite consensus tracking tasks with fixed and switching topologies. Firstly, an equivalent linearization data model of each agent is constructed using a pseudo partial derivative approach, where only one parameter needs to be estimated using input/output data of the controlled MASs. Meanwhile, the DoS attack behavior is described by a Bernoulli distribution process, and both cooperative and competitive relationships among agents are investigated. Moreover, an increment prediction compensator is designed to reduce the effect of DoS attacks. A data-based adaptive predictive bipartite consensus control algorithm is formulated. The corresponding theoretical analysis indicates that tracking errors of MASs with fixed and switching topologies converge to a small range around zero. Finally, several simulations and hardware tests further verify the proposed scheme’s effectiveness.
ISSN:0959-6518
2041-3041
DOI:10.1177/09596518241236928