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

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doi:10.4233/uuid:29f865ed-5af2-466e-ba4f-37a4c0b72c65

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

Doctoral Thesis (2024)

Author(s)

X. Liu (TU Delft - Team Bart De Schutter)

Contributor(s)

Bart De Schutter – Promotor (TU Delft - Delft Center for Systems and Control)

Azita Dabiri – Copromotor (TU Delft - Team Azita Dabiri, TU Delft - Team Bart De Schutter)

Research Group

Team Bart De Schutter

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

To reference this document use:

https://doi.org/10.4233/uuid:29f865ed-5af2-466e-ba4f-37a4c0b72c65

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Publication Year

2024

Language

English

Research Group

Team Bart De Schutter

ISBN (print)

978-90-5584-350-3

ISBN (electronic)

978-94-6384-659-2

Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

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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