Research Output
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 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.

  • Type:

    Article

  • Date:

    27 March 2024

  • Publication Status:

    In Press

  • Publisher

    SAGE Publications

  • DOI:

    10.1177/09596518241236928

  • ISSN:

    0959-6518

  • Funders:

    New Funder; National Natural Science Foundation of China

Citation

Halimu, Y., Zhao, H., Yu, H., Ding, S., & Qiao, S. (in press). Data-based adaptive predictive bipartite consensus for nonlinear multiagent systems against DoS attacks. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, https://doi.org/10.1177/09596518241236928

Authors

Keywords

Multiagent systems, bipartite consensus, data-driven control, predictive control, cyber attacks

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