Data-Driven Security Consensus Tracking of Multiple High-Speed Trains Under Random Topologies With Data Recovery Mechanism

Inspired by the challenges in improving train efficiency, this study investigates the consensus tracking of multiple high-speed trains (MHSTs) under random denial-of-service (DoS) attacks. First, a linearization method is used to linearize the nonlinear dynamic model of MHSTs with external running resistance. Second, the strategy of DoS attackers is elaborated on by using random variables obeying Bernoulli distributions. After establishing a data recovery mechanism, a security model-free adaptive control (MFAC) scheme is presented for MHSTs that is fully data-driven and independent of any model information or structural data of train groups. The effectiveness of this approach is theoretically analyzed without requiring accurate system modeling. Finally, the validity of MFAC and the impact of DoS attacks on MHSTs are evaluated through simulations involving G1868 HSTs between Guiyang North and Kaili South.