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

Author

Listed:
  • Fei Shang

    (Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China)

  • Jingyuan Zhan

    (College of Artificial Intelligence and Automation, Beijing University of Technology, Beijing 100124, China)

  • Yangzhou Chen

    (College of Artificial Intelligence and Automation, Beijing University of Technology, Beijing 100124, China)

Abstract

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.

Suggested Citation

  • Fei Shang & Jingyuan Zhan & Yangzhou Chen, 2020. "Energy-Saving Train Regulation for Metro Lines Using Distributed Model Predictive Control," Energies, MDPI, vol. 13(20), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:20:p:5483-:d:431691
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    References listed on IDEAS

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    1. Li, Shukai & Zhou, Xuesong & Yang, Lixing & Gao, Ziyou, 2018. "Automatic train regulation of complex metro networks with transfer coordination constraints: A distributed optimal control framework," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 228-253.
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    Cited by:

    1. Maryna Bulakh & Leszek Klich & Oleksandra Baranovska & Anastasiia Baida & Sergiy Myamlin, 2023. "Reducing Traction Energy Consumption with a Decrease in the Weight of an All-Metal Gondola Car," Energies, MDPI, vol. 16(18), pages 1-12, September.
    2. Jie Wang & Jin Xiao & Xiaoguang Hu, 2022. "Energy-Saving Applications Based on Train Mass Online Learning Using Time-Varying Train Model," Energies, MDPI, vol. 15(12), pages 1-13, June.

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