Dynamic event-triggered bipartite formation for multi-agent systems with false data injection attacks
ZHAO Hua-rong
PENG Li
XIE Lin-bo
YANG Jie-long
YU Hong-nian
Abstract：To address the issue of false data injection attacks in multi-input-multi-output nonlinear discrete-time multi-agent systems(MASs)with unknown dynamics models,this research presents a radial basis function neural network-based attack recognition scheme.Additionally,it proposes a dynamic event-triggered control strategy to address its communication-constrained problem.Firstly,a compact form dynamic linearization data model for the controlled system's input-output data is established at each working point of the agent using pseudo-derivative technology,and the estimation rule of the corresponding parameters of the model is provided.In addition,the bipartite formation control problem of the MASs is analyzed using signed graph theory,a combined measurement error equation is proposed to transform the bipartite formation control problem into a consensus control problem,and a dynamic event-triggered model-free adaptive bipartite formation control algorithm is proposed.Finally,the convergence of the bipartite formation tracking error is demonstrated,and the effectiveness of the algorithm is verified by simulation experiments.
Keywords：bipartite formationdata-driven controlfalse data injection attacksdynamic event-triggered
Publication Date：2025-05-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：10( 911-920 )