Intelligent Event Triggered Lane Keeping Security Control for Autonomous Vehicle Under DoS Attacks

This article addresses the issue of networked lane keeping security control for autonomous vehicles subject to aperiodic controller-targeted denial-of-service (DoS) attacks, taking into account time-varying driving speed and nonlinear tire cornering stiffness. To accurately estimate the incompletely measured states and capture the dynamic behaviors appearing at the end of aperiodic DoS attacks, full state gain adjustable switching observer is established for the fuzzy vehicle-road integrated dynamic systems obtained via the tensor product model transformation method. In order to ensure the control quality and simultaneously save the communication resource, new resilient adaptive event-triggered scheme is proposed with a reinforcement learning-based intelligent optimal threshold regulation mechanism based on observed states. Then, an augmented observer-based fuzzy switching system is constructed using the time delay method. In addition, sufficient conditions are established to guarantee global exponential stability of the closed-loop nonlinear lane keeping system with prescribed H∞ performance using a piecewise Lyapunov functional analysis approach. Subsequently, the gains for controller, observer, and trigger are co-designed and computed by solving certain matrix inequalities. Finally, the effectiveness of the proposed security control method is demonstrated through typical maneuver scenario in terms of reasonable triggered times, better tracking performances and acceptable lateral dynamics.