A Neural-Lyapunov-Based Adaptive Resilient Cruise Control of Platoons Subject to Cyber-Attacks on Leaders

In the realm of Intelligent Transportation Systems (ITSs), ensuring the safety and stability of connected automated vehicles (CAVs) is of paramount importance due to their susceptibility to vulnerabilities in interactions. The potential for system-wide disruption stemming from a cyber-attack on the leader underscores this need. Therefore, this letter introduces a nonlinear neural-Lyapunov-based adaptive resilient cruise control approach aimed at ensuring that all vehicles maintain safe tracking of the leader's profile, even in the presence of cyber-attacks and external disturbances. To achieve this, we employ an adaptive neural network to estimate the system's nonlinear characteristics. Subsequently, the control procedure is proposed, utilizing a virtual disturbance observer and Lyapunov theorem for stability analysis and adaptive laws to deal with nonlinearity, external disturbances, deception attacks, and singular control gain. Notably, our proposed approach eliminates the need for restrictive assumptions such as Lipschitz conditions on the nonlinear component and avoids the requirement for additional algorithms to switch between controllers in the event of a cyber-attack. This letter provides compelling evidence of system stability and the achievement of control objectives. Additionally, simulation and comparative results validate the theoretical analysis, highlighting the efficacy of the proposed methodology.