The vertical vibration generated by urban railway trains passing through uneven sections of steel rails is significant. This article proposes a pantograph-friendly automatic speed control framework for urban railway trains based on deep reinforcement learning. The impact of vertical vibration on the Rigid Contact Pantograph can be reduced by limiting the passing speed of urban rail trains in highly uneven sections of the rails. A coupling model between the pantograph and the overhead contact system under rigid contact is established to obtain the dynamic load on the pantograph under actual line operating conditions. Based on the industry standard recommended evaluation index for the severity of pantograph vibration, the “interval pantograph-friendly speed limit” is designed to represent the influence of the oncoming track on the vertical vibration amplitude of the pantograph. Then, a Twin Delayed Deep Deterministic Policy Gradient algorithm is intended for subway trains to learn driving strategies that comprehensively consider pantograph status, safety, and comfort. The experimental results show that compared with the traditional DRL control algorithm and the real data of Dalian Metro Line 2, the average vertical vibration of the pantograph is reduced by 3.57 and 8.83%, respectively. The TD3 framework is beneficial for improving the service life of the pantograph.


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    Titel :

    Deep Reinforcement Learning Based Automatic Speed Control Framework for Railway Trains with Rigid Contact Pantograph


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Zeng, Xiaoqing (Herausgeber:in) / Xie, Xiongyao (Herausgeber:in) / Sun, Jian (Herausgeber:in) / Ma, Limin (Herausgeber:in) / Chen, Yinong (Herausgeber:in) / Liu, Wangyang (Autor:in) / Feng, Qingsheng (Autor:in) / Zeng, Xiaoqing (Autor:in) / Han, Zhun (Autor:in)

    Kongress:

    International Symposium for Intelligent Transportation and Smart City ; 2025 ; Shanghai, China May 16, 2025 - May 17, 2025



    Erscheinungsdatum :

    20.07.2025


    Format / Umfang :

    7 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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