Adaptive Input Reconstruction Based Resilient MPC Against Deception Attacks
Ning He, Kai Ma, Huiping Li, Zhao Fan
Abstract
This article proposes an adaptive input reconstruction based resilient model predictive control (MPC) strategy for continuous-time nonlinear cyber-physical systems (CPS) against deception attacks. The input reconstruction mechanism is first developed based on the self-triggered sampling mechanism which could not only relax the assumptions on the attack energy limitation utilized by the existing resilient MPC methods, but also significantly reduce the cyber resource consumption of the resultant CPS. Besides, the adaptive prediction horizon mechanism is incorporated into the proposed MPC method to reduce its computational complexity. Furthermore, the feasibility and closed-loop stability of the developed MPC algorithm under deception attacks are strictly proven. Finally, the effectiveness of the designed algorithm in defending against deception attacks and reducing resource consumption is tested through both simulation and robot experiments.