Original scientific paper
https://doi.org/10.7307/ptt.v36i6.589
Online Model Predictive Control for Energy-saving Train Operation in Passenger-Freight Mixed Lines
Dandan WANG
; Beijing University of Technology, Faculty of Information Technology; Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent Systems
Heng DENG
; Beijing University of Technology, Faculty of Information Technology; Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent Systems
Jingyuan ZHAN
; Beijing University of Technology, Faculty of Information Technology; Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent Systems
*
Liguo ZHANG
; Beijing University of Technology, Faculty of Information Technology; Beijing University of Technology, Beijing Key Laboratory of Computational Intelligence and Intelligent Systems
* Corresponding author.
Abstract
This paper focuses on the online energy-saving operation control problem for passenger and freight trains running in a single-track railway line. Firstly, we design a centralised optimisation method to generate energy-saving reference profiles for both passenger and freight trains, in order to improve the punctuality of passenger trains and to reduce the total running time of freight trains in a central way. Secondly, we propose the distributed model predictive control (DMPC) based online trajectory optimisation problems for both types of trains, subject to their respective operational constraints including safety, punctuality, static speed limits and temporary speed restrictions. Then we formulate an online train operation control algorithm based on the centralised optimisation method for the initialisation of train trajectories and the DMPC method for the online trajectory planning. Finally, the proposed algorithm is applied to case studies of passenger and freight trains in a single track railway, and the numerical simulation results show that the proposed algorithm can realise online control for energy-saving train operation in the presence of input disturbances and temporary speed restrictions.
Keywords
optimal train control; energy-saving operation; distributed model predictive control; passenger and freight mixed lines
Hrčak ID:
324620
URI
Publication date:
20.12.2024.
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