On the Resilience of Autonomous Connected Vehicles Platoon Under DoS Attacks: a Predictor-Based Sampled Data Control

As wireless communication networks can be affected by cyber-attacks and communication delays, which lead to dangerous implications for cooperative driving safety, control design becomes crucial in order to provide both resilience and robustness to vehicular networks. To this aim, this article addresses autonomous connected vehicles platoon formation problem undergoing both communication delays and DoS attacks. The problem is solved via a novel distributed sampled-data predictor-based control, which exploits the classical model reduction approach in a distributed way so to compensate large input delays accounting for network latencies and malicious attack occurrence. The exponential stability of the vehicular network is analytically proven by exploiting Lyapunov-Krasovskii method, which provides stability conditions in the form of Linear Matrix Inequalities (LMIs). Numerical analysis confirm the effectiveness and the resilience of the theoretical derivation.