Resilient Self-Triggered Model Predictive Control of Cyber-Physical Systems Under Two-Channel False Data Injection Attacks

This paper presents a novel resilient self-triggered model predictive control (ST-MPC) method to alleviate potential threats of false data injection (FDI) attacks on cyber-physical systems (CPSs). First, considering that the data transmitted via the sensor-to-controller (S–C) and controller-to-actuator (C–A) channels in CPS may be tampered with by FDI attacks, a novel input reconstruction strategy combined with the ST-MPC mechanism is proposed to alleviate the threats of FDI attacks while reducing the computational and communication resources, in which key optimal control signals are selected and protected based on systematic performance inequalities. Correspondingly, a resilient ST-MPC algorithm combined with the dual-mode strategy is further proposed. Moreover, the iterative feasibility and the closed-loop stability are strictly demonstrated. Finally, the effectiveness of the proposed strategy is verified via a simulation study.