Resilient self-triggered predictive control for nonlinear system under dual-channel deception attacks

For nonlinear cyber physical systems (CPS) with additional disturbances and system constraint, a resilient self-triggered model predictive control (RST-MPC) strategy is developed, considering the impact of dual-channel deception attacks. The resilient set is first designed to strengthen the resilience of the controlled system against dual-channel deception attacks. Subsequently, the self-triggering mechanism is designed to reduce the computational and communication resource consumption while ensuring system security. Based on this, an RST-MPC algorithm is proposed. Furthermore, sufficient conditions are provided to guarantee the iterative feasibility of the RST-MPC algorithm and the closed-loop stability of the controlled system under dual-channel deception attack. Finally, the design goal of the developed algorithm is verified by the networked nonlinear system.