Resilient dual-mode model predictive control for constrained linear networked control systems with random DoS attacks

This paper proposes a resilient dual-mode model predictive control (MPC) framework that simultaneously considers system constraints and Denial-of-Service (DoS) attacks for linear time-invariant networked control systems (NCS). The DoS attacks is random and occurs at the both sensor-to-controller ( S - C ) communication channel and controller-to-actuator ( C - A ) communication channel. The MPC can well compensate the effect of the DoS attacks on the constrained system. At the same time, to address the problem of repeated oscillations caused by the inaccurate model near the equilibrium point of the MPC, and to reduce the computational effort, the resilient two-mode MPC is used. To guarantee the stability of NCS, a resilient terminal invariant set based on LMI is designed to ensure that the constrained system remains stable under M consecutive attacks. Finally, numerical simulations are conducted to validate the proposed MPC framework, showing its effectiveness in practical applications.