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, pp. 383-388, Sept. 2025.

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.

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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},
pages={383--388},
month=sep,
year={2025},
doi={10.1016/j.ifacol.2025.12.065}
}

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