A state reduction approach for learning-based model predictive control for train rescheduling


Reference:

C.F.O. da Silva, X. Liu, A. Dabiri, and B. De Schutter, "A state reduction approach for learning-based model predictive control for train rescheduling," Proceedings of the 1st IFAC Joint Conference on Computers, Cognition, and Communication (J3C 2025), Padova, Italy, Sept. 2025. To appear.

Abstract:

This paper proposes a state reduction method for learning-based model predictive control (MPC) for train rescheduling in urban rail transit systems. The state reduction integrates into a control framework where the discrete decision variables are determined by a learning-based classifier and the continuous decision variables are computed by MPC. Herein, the state representation is designed separately for each component of the control framework. While a reduced state is employed for learning, a full state is used in MPC. Simulations on a large-scale train network highlight the effectiveness of the state reduction mechanism in improving the performance and reducing the memory usage.

Bibtex entry:

@inproceedings{DaSLiu:25-018,
author={C.F.O. da Silva and X. Liu and A. Dabiri and B. {D}e Schutter},
title={A state reduction approach for learning-based model predictive control for train rescheduling},
booktitle={Proceedings of the 1st IFAC Joint Conference on Computers, Cognition, and Communication (J3C 2025)},
address={Padova, Italy},
month=sep,
year={2025},
note={To appear}
}


Go to the publications overview page.

This page is maintained by Bart De Schutter. Last update: September 1, 2025.