A decentralized robust control approach for virtually coupled train sets

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.