An MPC-based Distributed Bidirectional Control Strategy for Virtual Coupling with Unreliable Train-to-Train Communications

Virtual coupling (VC) is perceived to be promising in raising rail traffic capacity. In a train-to-train (T2T) based VC system, a communication network that ensures high quality of service (QoS) plays a critical role in enhancing both the coupling efficiency and the safety of the train platoon. However, unreliable communication environments characterized by issues such as time delays, packet loss, and network attacks present significant security risks to virtually coupled train sets (VCTS). How to cope with the impact caused by unstable communication and realize safe and stable VCTS formation are an important challenge for the VC system. In this paper, we propose a model predictive control (MPC) based distributed bidirectional control (DBC) strategy to tackle these challenges. We propose a control framework that integrates MPC with linear feedback-feedforward control to achieve real-time optimal control of the VC system, utilizing a bidirectional communication topology. To stabilize the VCTS, we derive local and string stability conditions to be satisfied by the controller parameters under asymmetric time-lagged unreliable networks, and utilize them as real-time constraints for the MPC controller. Furthermore, an analysis of the scalability of the proposed strategy has been conducted to improve its adaptability. Simulation results demonstrate that the proposed MPC-based DBC strategy significantly reduces the VCTS formation time and the maximum fluctuation of VCTS by 28.57% to 41.86%, and 28.84% to 52.10%, respectively, across various unreliable communication scenarios.