Distributed and Learning-based Model Predictive Control for Urban Rail Transit Networks
X. Liu (TU Delft - Team Bart De Schutter)
B. De Schutter – Promotor (TU Delft - Delft Center for Systems and Control)
A. Dabiri – Copromotor (TU Delft - Team Bart De Schutter, TU Delft - Team Azita Dabiri)
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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.