Security tracking control for discrete-time stochastic systems subject to cyber attacks

This paper is concerned with the security tracking problem under the quadratic cost criterion for a class of discrete-time stochastic linear networked control systems (NCSs) exposed to cyber attacks, covering false data injection attacks as well as a class of DoS attacks. Taking into account factors such as network congestion and the defensive role of intrusion detection systems, successful attack events are modeled as a Bernoulli random sequence. To describe the transient trajectory of an NCS under the impact of a random attack, a probabilistic definition of secure trackability is taken. Therefore, an observer -based dynamic output feedback controller is designed in order to achieve the specified probabilistic secure trackability. Specifically, the probabilistic safety output tracking problem is transformed into an input-to-state stability problem in the probabilistic sense for closed-loop systems with some new sufficient conditions, provided that an augmented incremental model is utilized Then, the controller parameters and the upper bound of the quadratic cost function are determined by solving matrix inequalities, while easy-solution forms of the matrix inequalities to be solved are presented by the Schur complementary lemma. Both simulation studies and practical experiments demonstrate the effectiveness of the proposed control scheme. (C) 2022 Published by Elsevier Ltd on behalf of ISA.

Automated summary

This paper addresses the security tracking problem for a class of discrete-time stochastic linear networked control systems subject to cyber attacks. The attacks considered include false data injection attacks and a class of DoS attacks. A probabilistic definition of secure trackability is proposed to describe the transient trajectory of the systems under random attacks. An observer-based dynamic output feedback controller is designed to achieve the specified probabilistic secure trackability. The problem is transformed into an input-to-state stability problem in the probabilistic sense, and matrix inequalities are solved to determine the controller parameters and the upper bound of the quadratic cost function.