Leader-Following Consensus Control of Unknown Nonlinear MASs Under False Data Injection Attacks
Meirong Wang, Jianqiang Hu, Ahmed Alsaedi, Jinde Cao
Abstract
This paper studies the distributed leader-following consensus problem of unknown nonlinear multi-agent systems (MASs) under false data injection attacks (FDIAs), where the followers connected to the leader may receive the injected false data from the leader's communication channels. Due to the existence of FDIAs, the real and broken leader state value is not available to the followers and cannot be used by followers' controllers, thus an attack compensator based on the errors between the predictive value and the actual measured value is added to the controller to mitigate the adverse effects of attacks. Fuzzy logic systems (FLSs) and Neural Network (NN) techniques are applied to approximate the unknown nonlinear dynamic by estimating the weight matrix. The proposed controller combines attack compensation with unknown nonlinear function compensation, and finally obtains sufficient conditions for the MASs to be ultimately uniformly bounded (UUB). Two algorithms are presented for undirected and directed communication topologies respectively and the simulation results verify the feasibility of the proposed consensus algorithms.