A data-driven MPC approach for virtually coupled train set with non-analytic safety distance

Abstract

Resorting to the emerging virtual coupling technology, multiple train units can operate as a virtually coupled train set (VCTS) to improve the flexibility and efficiency of train operations. To strictly guarantee collision avoidance, the space–time separation principle should be employed, where a non-analytic safety distance is used to safely separate units among VCTS. Thus, the formation control of VCTS is challenging, since it lacks analytic models to tune controllers with tracking accuracy and computational efficiency. To solve this problem, this paper proposes a data-driven model predictive control (DDMPC) approach. Based on a database with previously measured VCTS trajectories, we present a linear data-driven model to describe the non-analytic VCTS formation, such that the controller of DDMPC is yielded by solving a quadratic programming problem in a computationally efficient way. Next, to improve tracking accuracy, we optimize the modeling accuracy in the cost function of DDMPC, and bound the uncertainties from data-driven modeling and coupled states of VCTS. Furthermore, sufficient conditions are derived to guarantee constraint satisfaction and stability for VCTS. Finally, the advantages of the proposed DDMPC approach are demonstrated by comparing with several approaches in tracking accuracy and computational efficiency.