Tangent barrier Lyapunov function based adaptive event-triggered control for CPS under false data injection attacks

In this paper, an adaptive event-triggered control scheme is proposed for a class of continuous-time linear cyber-physical systems (CPSs) with unknown false data injection attacks (FDIA) and state constraints. First, the adaptive boundary estimation mechanism and Nussbaum-type function are introduced into the two-step backstepping control, which successfully reduces the impact of unknown attack gain. Second, considering that the Nussbaum function causes the chattering problem of the system state, the Tangent Barrier Lyapunov function (TBLF) is used to constrain the state. Then, the controller is designed in combination with the event-triggered mechanism (ETM), which saves communication resources. Based on the designed adaptive event-triggered security control method, all signals of the closed-loop system can be guaranteed to be bounded, and the state constraints are not violated. Finally, the simulation results verify the effectiveness and rationality of the proposed control strategy.