Mean-Square Bounded Cluster Consensus of Multi-Agent Networks Under Deception Attacks
This paper studies the mean-square bounded cluster consensus for non-linear multi-agent networks under deception attacks.Firstly,all network nodes are divided into different clusters.Considering that the control signal may be replaced by an error signal after being subjected to deception attacks,a Bernoulli random variable is introduced to represent the success of the deception attack.Secondly,a distributed impulsive controller employing pinning strategy is designed to ensure mean-square bounded cluster consensus in the presence of deception attacks.Furthermore,using the graph theory,linear matrix inequality and Lyapunov function method,sufficient conditions for realizing the mean-square bounded cluster consensus of multi-agent networks under deception attacks are given.Finally,a simulation example is supplied to verify the feasibility and effectiveness of the theoretical results.
Multi-agent networksdeception attackscluster consensuspinning strat-egyimpulsive control
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