Distributed and Learning-based Model Predictive Control for Urban Rail Transit Networks

Liu, X.

Distributed and Learning-based Model Predictive Control for Urban Rail Transit Networks

Doctoral thesis (2024)

Authors

X. Liu

Contributors

Bart De Schutter Delft Center for Systems and Control (supervisor)

A. Dabiri Team Azita Dabiri - Mechanical, Maritime and Materials Engineering , (advisor)

Model Predictive Control Distributed Control Learning-based Control Urban Rail Transit Network

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http://resolver.tudelft.nl/uuid:29f865ed-5af2-466e-ba4f-37a4c0b72c65

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Published Date

2024

Language

English

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Abstract

Urban rail transit networks are dedicated to providing safe, efficient, and eco-friendly transportation services for passengers. This thesis focuses on innovative model predictive control (MPC) strategies for the integration of passenger flows, timetables, and train speeds in urban rail transit networks. We introduce several innovative MPC frameworks, including bi-level MPC, scenario-based distributed MPC, learning-based MPC, and cooperative distributed MPC. These approaches exhibit significantly improved performance compared to conventional methods.

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