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基于Sarsa算法的城轨列车节能控制策略研究

孟建军 蒋小一 陈晓强 胥如迅

铁道标准设计2024,Vol.68Issue(8):8-14,7.
铁道标准设计2024,Vol.68Issue(8):8-14,7.DOI:10.13238/j.issn.1004-2954.202212050008

基于Sarsa算法的城轨列车节能控制策略研究

Intelligent Control Strategy of Urban Rail Train Based on Sarsa Algorithm

孟建军 1蒋小一 2陈晓强 3胥如迅3

作者信息

  • 1. 兰州交通大学机电技术研究所,兰州 730070||甘肃省物流与运输装备行业技术中心,兰州 730070||甘肃省物流及运输装备信息化工程技术研究中心,兰州 730070
  • 2. 兰州交通大学机电技术研究所,兰州 730070
  • 3. 兰州交通大学机电技术研究所,兰州 730070||甘肃省物流与运输装备行业技术中心,兰州 730070||甘肃省物流及运输装备信息化工程技术研究中心,兰州 730070||兰州交通大学机电工程学院,兰州 730070
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摘要

Abstract

Focusing on the problem of energy-efficient operation of urban rail transit,an energy-efficient control strategy of urban rail transit based on Sarsa reinforcement learning algorithm is proposed.To achieve the driving strategy of reducing energy consumption while taking into account punctuality and comfort when the urban rail transit is in automatic driving mode and facing different road conditions,the running state of the train is discretized and the continuous driving process is divided into several sub-sections according to the line conditions.Combined with the speed limit,initial state and terminal state of the track,the appropriate reward functions are constructed based on energy consumption and running time.At the same time,the setting of optional speed is limited by the maximum speed and minimum speed that can be reached in the current state,which reduces the exploration space and accelerates the convergence of the algorithm.Finally,the simulation is carried out for the case of Xiaohongmen-Xiaocun Station on the Yizhuang Urban Rail Line in Beijing.The experimental results show that,compared with the traditional dynamic programming method,the Sarsa algorithm saves 9.32%energy while meeting the requirements of comfort and punctuality.Compared with the Q-learning algorithm in reinforcement learning,the number of overspeed also decreases significantly in the process of speed selection.Simulation results show that the Sarsa algorithm has a better energy-saving effect and security.With the algorithm parameters unchanged,the speed limit conditions are adjusted,and compared with the traditional dynamic programming method again,it still saves 4.21%energy,which verifies the robustness of the algorithm.

关键词

城市轨道交通/节能/强化学习/Sarsa算法/控制策略

Key words

urban rail trains/energy-efficient/reinforcement learning/Sarsa algorithm/control strategy

分类

交通工程

引用本文复制引用

孟建军,蒋小一,陈晓强,胥如迅..基于Sarsa算法的城轨列车节能控制策略研究[J].铁道标准设计,2024,68(8):8-14,7.

基金项目

国家自然科学基金项目(62063013) (62063013)

兰州交通大学青年基金项目(2021018) (2021018)

甘肃省优秀研究生"创新之星"项目(2022CXZX-517) (2022CXZX-517)

铁道标准设计

OA北大核心

1004-2954

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