Learning-Based Secure Control for Multichannel Networked Systems Under Smart Attacks

This article is concerned with the security control of a class of discrete-time linear cyberphysical systems (CPSs) subject to denial-of-service attacks. To enhance the inherent resistance of the CPS against damage and attacks, a multichannel network is employed for remote information interaction between the ingredients of the system. In this way, a complex interaction process will be formed between the signal sender and the smart malicious adversary. Specifically, in the context of using the multichannel network, the malicious adversary has to maximize the attack success probability within its energy constraint. As a counterpart, the system tries to mitigate the negative impact of such attacks on CPS control performance. For the purpose of designing a control strategy to cope with the attacks, this interaction process is formulated as a zero-sum stochastic game, while the Nash equilibrium solution of this problem is found with the help of the proposed learning algorithm, and the optimal mixed strategies for both attackers and defenders are derived. Furthermore, for the CPS driven by the obtained decision-making strategies, a Kalman filter-based active dynamic output feedback resilient controller is proposed. Finally, the effectiveness of the developed optimal defense strategies and the resilient controller is demonstrated by extensive case studies on the servo motor experimental platform.