Weighted Train-to-Train Communication-Based Data-Driven Consensus Tracking of Multiple HSTs Subject to Deception Attacks

Cyber-security is widely regarded as the main factor affecting the cooperation efficiency of multiple high-speed trains (MHSTs). In this work, based on the proposed weighted train-to-train (T2T) communication network, a consensus tracking scheme for MHSTs subject to malicious deception attacks is proposed, which is a data-driven control approach. Firstly, a deception attack model along with a T2T communication protocol incorporating weight factors is introduced, and followed by the presented distributed model free adaptive control (MFAC) scheme for MHSTs. Additionally, in the extended research, a novel priority-based attack strategy is investigated, and a compensation algorithm to counter deception attacks is also established for the first time. Finally, Simulink and StarSim hardware-in-loop (HIL) semi-physical platforms are employed to validate the findings presented in this study.