Hierarchical Attack Identification for Distributed Robust Nonlinear Control. Issue 2 (2020) - Braun, Sarah; Albrecht, Sebastian; Lucia, Sergio
Abstract: Developing tools for attack identification in large-scale networked control systems is a research area of increasing significance for the secure and reliable operation of autonomous control systems. Due to scalability limits and privacy issues of individual subsystems, attack identification methods should not rely on global model knowledge. We address systems of interconnected nonlinear subsystems with coupled dynamics or constraints in a distributed control setup. The local controllers share information about the coupling variables of the subsystems and are designed to be robust towards attacks and uncertain influences through neighboring subsystems. We present a scalable hierarchical attack identification method which monitors the evolution of the coupling variables after an attack occurred in some unknown subsystem. Based on the mutual exchange of local sensitivity information among the subsystems, the propagation of the attack through the network is approximated. The propagation equations are used to formulate a quadratic program whose solution determines the attack signal that explains the observed network evolution best. The developed approach is applied to the IEEE 30 bus system to illustrate attack identification in power systems with faulty buses.