LAUSR

This paper investigates the secure control problem for cyber-physical systems when the malicious data is injected into the cyber realm which is directly connecting to the actuators. Based on moving target defense and reinforcement learning, we propose a novel proactive and reactive defense control scheme. First, the system $(A,B)$ is modeled as a switching system consisting of several controllable pairs $(A,\mathcal{B}_{l})$ to facilitate the construction of the moving target defense control scheme. The controllable pairs $(A,\mathcal{B}_{l})$ can be altered to update system dynamics under certain unpredictable switching probabilities for each subsystem, which can prevent the adversaries from effective attacks. Second, both attack detection and isolation schemes are designed to accurately locate and exclude the compromised actuators from a switching sequence. Third, a reinforcement learning algorithm based on the zero-sum game theory is proposed to design the defense control scheme when there exist no controllable subsystems to switch. To demonstrate the effectiveness of the defense control scheme, a three-tank system under unknown cyber attacks is illustrated.