Data-based adaptive predictive bipartite consensus for nonlinear multiagent systems against DoS attacks
Authors: Halimu, Y., Zhao, H., Yu, H., Ding, S., Qiao, S.
Journal: Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering
Publication Date: 01/08/2024
Volume: 238
Issue: 7
Pages: 1231-1241
eISSN: 2041-3041
ISSN: 0959-6518
DOI: 10.1177/09596518241236928
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.
Source: Scopus