铁道标准设计2024，Vol.68Issue(7)：185-191,217,8.DOI:10.13238/j.issn.1004-2954.202211110006
城轨列车深度强化学习节能优化控制方法
Energy-saving Optimization Control Method for Reinforced Learning of Urban Rail Train
摘要
Abstract
In order to improve the control performance of automatic train operation(ATO)of urban rail trains,the problem of frequent condition switching and high traction energy consumption in ATO target speed profile tracking control method for urban rail is addressed.The DQN control method uses train time redundancy(TR)as the active constraint for optimizing train punctuality,stopping precision,and energy consumption.A model of rail train dynamics and an optimal fitness function are developed for urban rail trains.The TR planning reference system constraint is applied to the DQN train controller,and the action space and reward function are defined.Finally,a stochastic gradient descent algorithm is used to update the parameters of the Q-network in the train controller neural network.The results show that adding the TR-DQN algorithm constrained by the TR time-planning reference system improves the convergence speed of DQN iterations and the stability of the iterative training process.The TR-DQN control method for dynamically adjusting the train operation strategy consumes 12.32% less energy than that of the traditional target speed profile tracking PID algorithm,and the frequency of switching between train stations is lower.The energy consumption of train traction is reduced by 7.5% and 6.4% in turn for the three different planning times set between stations.The frequency of dynamic switching of train conditions between stations and traction energy consumption decrease with the increase of trip planning time.关键词
城市轨道交通/列车自动驾驶/时间规划系统/节能运行/深度强化学习/DQN算法Key words
urban rail/automatic train operation/time planning system/energy-saving operation/deep reinforced learning/DQN algorithm分类
交通工程引用本文复制引用
郭啸,孟建军,陈晓强,胥如迅,李德仓,宋明瑞..城轨列车深度强化学习节能优化控制方法[J].铁道标准设计,2024,68(7):185-191,217,8.基金项目
国家自然科学基金项目(72061021、62063013) （72061021、62063013）
甘肃省优秀研究生"创新之星"项目(2022CXZX-517) （2022CXZX-517）
