Data-Driven Event-Triggered Sliding Mode Secure Control for Autonomous Vehicles Under Actuator Attacks

This article investigates a comprehensive data-driven event-triggered secure lateral control of autonomous vehicles under actuator attacks. We consider stabilization issues of autonomous vehicles subject to modeling difficulties, limited communication resources, and actuator attacks. The dynamic model decomposition (DMD) from data is exploited to characterize the inherent lateral dynamics model of autonomous vehicles, the event-triggered transmission scheme is utilized to alleviate communication burden for limited bandwidth network, and the sliding mode control scheme is designed to ensure the security of autonomous vehicles under actuator attacks. The stability analysis and the stabilization method as well as its algorithm are presented. The proposed secure control scheme can actively counteract the malicious effects caused by actuator attacks and integrates the advantages of both data-driven modeling and model-based control design. Finally, several comparative case studies show the effectiveness of the proposed secure control scheme.