Event-based adaptive compensation control of nonlinear cyber-physical systems under actuator failure and false data injection attack

This paper studies the event-triggered adaptive compensation control problem of nonlinear cyber-physical systems under false data injection (FDI) attack and actuator failure. Firstly, in order to save the limited network resources, a new adaptive event triggering scheme (AETS) is presented, whose threshold can be adjusted according to the change of system state. Secondly, an observer based on neural networks is designed. Then, we design an event-triggered adaptive controller and adaptive laws to effectively compensate for FDI attack and actuator failure. Furthermore, through the system stability analysis, the result shows that the tracking error can converge exponentially to a compact set with an adjustable radius. Finally, the theoretical results are verified by the manipulator system example.