A Secure Adaptive Resilient Neural Network-Based Control of Heterogeneous Connected Automated Vehicles Subject to Cyber Attacks

Abstract

In the realm of intelligent transportation systems (ITSs), safeguarding the resilience of connected automated vehicles (CAVs) with vulnerable interactions is imperative, particularly amidst the rapid spread of cyber-attack effects within the system. This paper introduces a pioneering Neural Network-based Cooperative Adaptive Resilient Control (NNCARC) approach that seamlessly integrates adaptive neural networks and resilient control mechanisms to counteract the impacts of nonlinearity, cyber-attacks, and external disturbances. The methodology commences with the development of an adaptive neural network to precisely estimate system nonlinearity, followed by the proposal of a cooperative adaptive resilient control strategy leveraging the Lyapunov theorem for stability analysis and adaptive laws. To the authors’ knowledge, this is the first time that a NNCARC is proposed which ensures all vehicles within a platoon, with any type of network topology, adhere safely to the leader's time-varying profile, without necessitating additional controller switching algorithms in the event of a cyber-attack. By eliminating restrictive assumptions like the Lipschitz condition on nonlinear components, the proposed methodology enhances its versatility and robustness. Theoretical analyses validate system stability and objective achievement, while simulation studies across diverse network topologies, cyber-attack scenarios, and external disturbances substantiate the efficacy of the approach in controlling CAVs within a platoon. This paper constitutes a significant advancement in resilient control methodologies for CAVs, offering a comprehensive solution to mitigate cyber-attack and disturbance effects while ensuring system stability and performance.