Recovery-Based Distributed Adaptive ILC With Fading Compensation for MHSTs Under DoS Attacks: A Model-Free Approach
Wei Qin Yu, Deqing Huang, Xiao‐Lei Wang, Kai Jun Xu · IEEE Transactions on Intelligent Transportation Systems · 2024
The coordination of multiple high-speed trains (MHSTs) can improve the transportation efficiency and the safety performance. However, the complicated dynamic characteristics of MHSTs and the unreliable train-to-train (T2T) (wireless) communication modes are challenging the conventional control approaches. Focusing on the adverse impact from denial-of-service (DoS) attacks and faded channels caused by the T2T networks, the study designs a distributed adaptive iterative learning controller (DAILC) with recovery and compensation mechanisms, which is a model-free approach. Relying on the novel equivalent linearization strategy, a DAILC is established by using the distributed tracking errors, and the theoretical analysis has verified the complete tracking performance of MHSTs. The results in simulation test demonstrate the feasibility of the proposed DAILC and the effectiveness of the recovery and compensation mechanisms.