Robust event-triggered model predictive control for cyber-physical systems under denial-of-service attacks

This paper investigates the resilient control problem for constrained continuous-time cyber-physical systems subject to bounded disturbances and denial-of-service (DoS) attacks. A sampled-data robust model predictive control law with a packet-based transmission scheduling is taken advantage to compensate for the loss of the control data during the intermittent DoS intervals, and an event-triggered control strategy is designed to save communication and computation resources. The robust constraint satisfaction and the stability of the closed-loop system under DoS attacks are proved. In contrast to the existing studies that guarantee the system under DoS attacks is input-to-state stable, the predicted input error caused by the system constraints can be dealt with by the input-to-state practical stability framework. Finally, a simulation example is performed to verify the feasibility and efficiency of the proposed strategy.