Neural network adaptive consensus control for nonlinear multi-agent systems encountered sensor attacks

Abstract

This paper investigates the neural network (NN) adaptive consensus output-feedback control problem for a class of nonlinear multi-agent systems (MASs) encountered sensor attacks. To overcome the impact of unknown sensor attacks, a NN estimation algorithm is adopted to estimate the unknown sensor attacks. Subsequently, a novel NN observer is established to estimate the states of encountered sensor attacks. Consequently, under the framework of backstepping control design, an adaptive NN consensus control method is proposed. By using the Lyapunov stability theory, the proposed consensus control method can not only ensure that all the signals of controlled MASs remain bounded, but also make all followers maintain consensus with the trajectory of the leader. Simulation results and comparative results illustrate the effectiveness of the proposed consensus control scheme.