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

Caio Fabio Oliveira da Silva; Xiaoyu Liu; Azita Dabiri; Bart De Schutter

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arXiv · 2504.20233

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

Caio Fabio Oliveira da Silva·Xiaoyu Liu·Azita Dabiri·Bart De Schutter

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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 RIS

Caio Fabio Oliveira da Silva, Xiaoyu Liu, Azita Dabiri, Bart De Schutter. 2025-04-28. A state reduction approach for learning-based model predictive control for train rescheduling. https://arxiv.org/abs/2504.20233

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