A Cyber-Security Framework for ST-MPC of State and Input Constrained CPS Under False Data Injection Attacks

Abstract

Self-triggered model predictive control (ST-MPC) is widely applied in various aspects currently, however, the ST-MPC mechanisms that have seldom been developed consider the possible malicious false data injection (FDI) attacks in the cyber-physical system (CPS). Therefore, in this paper, a novel resilient ST-MPC strategy based on input reconstruction (IR) against FDI attacks is proposed for a nonlinear input-affine discrete-time system with state and input constraints, which combines both cyber security and resource consumption. More specifically, when faced with FDI attacks in controller-to-actuator (C-A) channels at the triggering instants, on the actuator side, two key control data are selected to reconstruct input control signals for application into the system, otherwise, the optimal input control signals will be applied into the controlled system. Furthermore, a resilient ST-MPC algorithm with a dual-mode control strategy is proposed, and its closed-loop stability is also analyzed, in which the state constraint is elaborated. Finally, a simulation and its resultant comparisons illustrate the effectiveness of the proposed method.