This paper proposes a train energy-saving control strategy based on Sarsa( ) reinforcement learning algorithm for urban rail transit. The strategy aims to achieve energy-saving driving while taking into account both timeliness and comfortability when the urban rail train is in autonomous driving mode. Finally, a simulation example is given for Beijing Railway Yizhuang Line Xiaohongmen to Xiao Village Station. Experimental results show that compared with traditional dynamic programming methods, Sarsa( ) algorithm can save 3.70% energy under the premise of meeting comfortability and timeliness requirements. The simulation results prove that Sarsa( ) algorithm has better energy-saving effect.


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    Titel :

    Intelligent Control Strategy of Urban Rail Train Based on Sarsa(λ) Algorithm


    Beteiligte:
    Jiang, Xiaoyi (Autor:in) / Shi, Kun (Autor:in) / Liu, Yatong (Autor:in) / Liu, Yong (Autor:in)


    Erscheinungsdatum :

    19.01.2024


    Format / Umfang :

    1714745 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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