Output based resilient model predictive control of Cyber–Physical Systems under cover attacks

Abstract

In this article, a state estimate based robust model predictive control strategy is developed for networked cyberphysical systems under false data injections. The core of the proposed approach consists in elaborating an observed based general framework whose main feature relies on its adaptability to different classes of attacks. The main feature of the proposed scheme relies on the capability to mitigate undesired system behaviors due to external malicious actions. This is achieved by showing that an augmented state-estimate/estimation error set-membership conditions allow us to quickly detect data integrity anomalies and, as a consequence, to implement adequate counter-measures. In particular, set-theoretic model predictive arguments are combined with the perturbation analysis and sequential quadratic programming in order to reduce as much as possible the occurrence of refresh procedures on the communication network when resilient command actions are no longer available. As one of its main merits, these arguments allow to remove the constructive assumptions present in very recent competitors. In this respect, the framework is customized for covert attacks by specifying actuation and detection phases and proving feasibility and closed-loop stability properties. The core of the resulting solution exploits output feedback receding horizon and set-theoretic control ideas so that the resilient nature of the virtual control moves is formally put in light.