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

Halimu, Yeerjiang; Zhao, Huarong; Yu, Hongnian; Ding, Shuchen; Qiao, Shangling

Abstract

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.

• Journal Article Type: Article
• Acceptance Date: Feb 9, 2024
• Online Publication Date: Mar 27, 2024
• Deposit Date: Apr 5, 2024
• Journal: Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering
• Print ISSN: 0959-6518
• Electronic ISSN: 2041-3041
• Publisher: SAGE Publications
• Peer Reviewed: Peer Reviewed
• DOI: https://doi.org/10.1177/09596518241236928
• Keywords: Multiagent systems, bipartite consensus, data-driven control, predictive control, cyber attacks
• Public URL: http://researchrepository.napier.ac.uk/Output/3586886