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

Doctoral Thesis (2024)
Authors

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

Research Group
Team Bart De Schutter
More Info
expand_more
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
DOI:
https://doi.org/10.4233/uuid:29f865ed-5af2-466e-ba4f-37a4c0b72c65
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.

Files

License info not available
warning

File under embargo until 23-10-2025