A state reduction approach for learning-based model predictive control for train rescheduling
Fuente:
arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913812072890368 |
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| author | da Silva, Caio Fabio Oliveira Liu, Xiaoyu Dabiri, Azita De Schutter, Bart |
| author_facet | da Silva, Caio Fabio Oliveira Liu, Xiaoyu Dabiri, Azita De Schutter, Bart |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_20233 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A state reduction approach for learning-based model predictive control for train rescheduling da Silva, Caio Fabio Oliveira Liu, Xiaoyu Dabiri, Azita De Schutter, Bart Systems and Control Optimization and Control 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. |
| title | A state reduction approach for learning-based model predictive control for train rescheduling |
| topic | Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2504.20233 |