Adaptive neural network dynamic event-triggered consensus control for nonlinear multi-agent systems subject to sensor deception attacks and actuator faults

This paper presents an adaptive neural network dynamic event-triggered consensus method for nonlinear multi-agent systems (MASs) under sensor deception attacks and actuator faults. A single parameter learning method is integrated into backstepping technique to simplify the design procedure. The neural network is utilized to compensate for the unknown dynamics of MASs. The designed controller can withstand sensor deception attacks and accommodate actuator faults. Moreover, the dynamic event-triggered mechanism is designed to conserve communication resources. The designed control law ensures that all signals of the MASs are uniformly bounded. An expository simulation example reveals the virtue of the presented method.