In this paper, a novel approach to train control systems based on virtual coupling is presented. Virtual coupling is a concept that has evolved from platooning of vehicles and allows to reduce the distance and headway between trains without constructing new lines while ensuring safe operation. With this objective in mind, we propose a decentralized robust model predictive control (MPC) framework for a virtually coupled train set based on a min–max approach. Unlike the nominal MPC, robust MPC is designed to consider external undetermined disturbances and errors to improve robustness in real‐world applications. Therefore, in this study, we present the formulation of a robust MPC based on solving a finite‐horizon optimization problem with bounded uncertainties. The bounds consider resistive modeling errors, positioning errors, communication delays, and a possible adhesion loss of up to 10%. We then performed four simulations to compare the behavior of the robust MPC with the equivalent nominal MPC. In these simulations, we simulated a metro line, main line, and high‐speed line. The simulations also analyzed the behavior of the robust MPC under the considered perturbations and different communication delays. The results show that the robust MPC ensures safer operation than nominal MPC in subways, conventional lines, and high‐speed lines. Future research can focus on centralized MPC and artificial intelligence.

In recent decades, the demand for rail transport has been growing steadily and faces a double problem. Not only must the transport capacity be increased, but also a more flexible service is needed to meet the real demand. Both objectives can be achieved through virtual coupling (VC), which is an evolution of the current moving block systems. Trains under VC can run much closer together, forming what is called a virtually coupled train set (VCTS). In this paper, we propose an approach in which virtual coupling is implemented via model predictive control (MPC). For this purpose, we define a robust controller that can predict, based on a dynamic model of the train, the state of the system at later moments of time and make the appropriate control decisions. A robust MPC (RMPC) is obtained by introducing two uncertain variables. The first uncertain variable is added to the acceleration equation of the dynamic model, while the second uncertain variable is used to define the uncertainty in the train positioning. To test the RMPC for virtual coupling, two simulation cases are performed for a metro line, analysing the influence of both the uncertainties. In all cases, the results obtained show a safer operation of the virtual coupling without significantly affecting the service.

The current mobility situation is constantly changing as people are increasingly moving to urban areas. Therefore, a flexible mode of transport with high-capacity passenger trains and a high degree of modularity in the trains’ composition is necessary. Virtual coupling (VC) is a promising solution to this problem because it significantly increases the capacity of a line and provides a more flexible mode of operation than conventional signaling systems. This novel review, in which approximately 200 papers were analyzed, identifies the main topics of current railway-related VC research, and represents the first step toward the implementation of VC in future railways. It was found that industry research has mainly focused on the feasibility of VC implementation and operation, whereas in academia, which is coordinated with industry, research has focused on control and communication systems. From a technological perspective, the main challenges for VC were identified with regard to aspects such as safety, control technology, interlocking, vehicle-to-vehicle communication, cooperative train protection and control, and integrated traffic management. The important directions for future research that have been identified for future development include complete dynamic models, real-time controllers, reliable and secure communication, different communication topologies, cybersecurity, intelligent control, reinforcement learning, and Big Data analytics.