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Open AccessArticle

Energy-Saving Train Regulation for Metro Lines Using Distributed Model Predictive Control

1
Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China
2
College of Artificial Intelligence and Automation, Beijing University of Technology, Beijing 100124, China
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Author to whom correspondence should be addressed.
This paper is an extension of the conference paper: “Distributed Model Predictive Control for Train Regulation in Urban Metro Transportation” appeared in the Proceedings of the 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA, 4–7 November 2018; pp. 1592–1597.
Energies 2020, 13(20), 5483; https://doi.org/10.3390/en13205483
Received: 19 August 2020 / Revised: 1 October 2020 / Accepted: 7 October 2020 / Published: 20 October 2020
(This article belongs to the Section Smart Grids and Microgrids)
Due to environmental concerns, the energy-saving train regulation is necessary for urban metro transportation, which can improve the service quality and energy efficiency of metro lines. In contrast to most of the existing research of train regulation based on centralized control, this paper studies the energy-saving train regulation problem by utilizing distributed model predictive control (DMPC), which is motivated by the breakthrough of vehicle-based train control (VBTC) technology and the pressing real-time control demand. Firstly, we establish a distributed control framework for train regulation process assuming each train is self-organized and capable to communicate with its preceding train. Then we propose a DMPC algorithm for solving the energy-saving train regulation problem, where each train determines its control input by minimizing a constrained local cost function mainly composed of schedule deviation, headway deviation, and energy consumption. Finally, simulations on train regulation for the Beijing Yizhuang metro line are carried out to demonstrate the effectiveness of the proposed DMPC algorithm, and the results reveal that the proposed algorithm exhibits significantly improved real-time performance without deteriorating the service quality or energy efficiency compared with the centralized MPC method. View Full-Text
Keywords: metro line; train regulation; energy saving; distributed; model predictive control; operational constraints metro line; train regulation; energy saving; distributed; model predictive control; operational constraints
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MDPI and ACS Style

Shang, F.; Zhan, J.; Chen, Y. Energy-Saving Train Regulation for Metro Lines Using Distributed Model Predictive Control. Energies 2020, 13, 5483.

AMA Style

Shang F, Zhan J, Chen Y. Energy-Saving Train Regulation for Metro Lines Using Distributed Model Predictive Control. Energies. 2020; 13(20):5483.

Chicago/Turabian Style

Shang, Fei; Zhan, Jingyuan; Chen, Yangzhou. 2020. "Energy-Saving Train Regulation for Metro Lines Using Distributed Model Predictive Control" Energies 13, no. 20: 5483.

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