Resilient Prediction Event-Triggered Control Based on Attack Detection for Cyber-Physical Systems

Abstract

This article focuses on cyber-physical systems subject to unknown disturbances and denial-of-service attacks. To ensure input-to-state stability, an event-triggered predictive control update scheme based on attack detection and a predictor is proposed. Firstly, a attack detection strategy is adopted, which makes full use of historical signals to detect whether the system is under attack at the current moment. Then, an attack detection-based control update scheme is proposed to compensate for state loss, and an event-triggered mechanism integrating detection and prediction is established to reduce resource consumption while ensuring system stability. The results show that the closed-loop cyber-physical systems can achieve input-to-state stability under the proposed control update scheme and event-triggered mechanism. An important advantage of the proposed control update scheme is that the cyber-physical systems select different state inputs according to the attack status at the event-triggered moment, thereby enabling the system to tolerate more adverse denial-of-service attacks. Finally, a simulation case is provided to verify the effectiveness of the proposed method.