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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    Title :

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


    Contributors:
    Jiang, Xiaoyi (author) / Shi, Kun (author) / Liu, Yatong (author) / Liu, Yong (author)


    Publication date :

    2024-01-19


    Size :

    1714745 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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