Automatic Driving Operation Strategy of Urban Rail Train Based on Improved DQN Algorithm

To realize a better automatic train driving operation control strategy for urban rail trains,an automatic train driving method with improved DQN algorithm(classical deep reinforcement learning algorithm)is proposed as a research object.Firstly,the train control model is established by considering the train operation requirements.Secondly,the dueling network and DDQN ideas are introduced to prevent the value function overestimation problem.Finally,the priority experience playback and“restricted speed arrival time”are used to reduce the useless experience utilization.The experiments are carried out to verify the train operation strategy method by simulating the actual line conditions.From the experimental results,the train operation meets the ATO requirements,the energy consumption is 15.75%more energy-efficient than the actual operation,and the algorithm convergence speed is improved by about 37%.The improved DQN method not only enhances the efficiency of the algorithm but also forms a more effective operation strategy than the actual operation,thereby contributing meaningfully to the advancement of automatic train operation intelligence.

High-Speed Trains Automatic Operation with Protection Constraints: A Resilient Nonlinear Gain-based Feedback Control Approach

This paper addresses the control design for automatic train operation of high-speed trains with protection constraints.A new resilient nonlinear gain-based feedback control approach is proposed,which is capable of guaranteeing,under some proper non-restrictive initial conditions,the protection constraints control raised by the distance-to-go(moving authority)curve and automatic train protection in practice.A new hyperbolic tangent function-based model is presented to mimic the whole operation process of high-speed trains.The proposed feedback control methods are easily implementable and computationally inexpensive because the presence of only two feedback gains guarantee satisfactory tracking performance and closed-loop stability,no adaptations of unknown parameters,function approximation of unknown nonlinearities,and attenuation of external disturbances in the proposed control strategies.Finally,rigorous proofs and comparative simulation results are given to demonstrate the effectiveness of the proposed approaches.

JR East aims for driverless Shinkansen operation

East Japan Railway Company(JR East)is aiming to“realize driverless train operation”as one of the key measures to respond to rapid changes in the business environment.Currently,Automatic Train Operation(ATO)equipment is not installed on the Shinkansen,but there are plans to introduce ATO or driverless operation in the near future.From 2018-2021,the Ministry of Land,Infrastructure,Transport and Tourism(MLIT)held the“ATO Technology Study Group for Railways”in which the concept of technical requirements necessary for driverless operation was discussed.In 2021,JR East conducted the GOA4 demonstration test on the Joetsu Shinkansen.In this test,we were able to confirm the basic functions of Shinkansen vehicles such as automatic departure control,speed control,fixed position stop control,and remote stop control using ATO.We aim to realize unattended operation(GOA4)for deadhead trains between Niigata Station and the Niigata Shinkansen Rolling Stock Center by the end of the 2020 s,and driverless operation(GOA3)for passenger trains of the Joetsu Shinkansen by the mid-2030s and continue to develop the necessary technologies and build systems.

改进麻雀算法在列车ATO多目标优化中的应用

针对列车自动驾驶(ATO)运行过程多目标优化问题,以列车运行安全性、列车动力学模型等因素为约束条件,考虑列车准时性、能耗、舒适性等指标,使用模糊隶属度法建立多目标优化模型。利用罚函数处理约束条件,将停车误差与限速作为惩罚项并构造出适当的惩罚函数加入到目标函数中,从而得到增广目标函数,提出基于改进麻雀算法(ISSA)的求解策略。为提高麻雀算法(SSA)的全局寻优能力,避免收敛于局部最优,引入Logistic映射、自适应超参数、变异算子对传统麻雀算法进行改进,通过测试函数对算法性能进行验证,表明ISSA算法的收敛速度、寻优精度比传统SSA算法好。以工况转换点为决策变量,通过ISSA算法对工况转换点的位置及速度进行寻优,进而获得目标速度-距离曲线。最后选取城轨车辆参数与线路数据进行仿真验证,仿真结果表明:所提优化策略相较于未优化前,舒适性提高了21.22%,能耗降低了22.41%,准时性与停车误差满足要求;与PSO优化方法相比,收敛速度更快,运行时间几乎一样的情况下能耗降低了12.74%,节能效果更佳;停车误差降低了20.45%,舒适性保持在舒适范围之内;对于速度-距离曲线,巡航距离更长、惰行距离变短、最高运行速度降低。由此可见,达到了综合优化ATO的目的,验证了ISSA优化策略的有效性。

Braking distance prediction for vehicle consist in low-speed on-sight operation:a Monte Carlo approach

The first and last mile of a railway journey, in both freight and transit applications, constitutes a high effort and is either non-productive(e.g. in the case of depot operations) or highly inefficient(e.g. in industrial railways). These parts are typically managed on-sight, i.e. with no signalling and train protection systems ensuring the freedom of movement. This is possible due to the rather short braking distances of individual vehicles and shunting consists. The present article analyses the braking behaviour of such shunting units. For this purpose, a dedicated model is developed. It is calibrated on published results of brake tests and validated against a high-definition model for lowspeed applications. Based on this model, multiple simulations are executed to obtain a Monte Carlo simulation of the resulting braking distances. Based on the distribution properties and established safety levels, the risk of exceeding certain braking distances is evaluated and maximum braking distances are derived. Together with certain parameters of the system, these can serve in the design and safety assessment of driver assistance systems and automation of these processes.

珠三角城际CTCS2+ATO列控系统控车舒适性研究及案例分析

针对珠三角城际线上CRH6A-0405动车组装备的CTCS2-200K+ATO列控车载设备,在控制列车减速进站过程中舒适性欠佳问题,研究ATO系统的工作原理及其控车策略、ATO系统所使用的PID滑模控制器及其模型和相关参数,以及ATO系统控车舒适性的评价指标及影响因素。对列车进站进入降速区前存在异常加速而导致的冲击率过大问题,采取降低列车高速运行时的滑模增益系数避免控制命令饱和的优化方案;对站前减速过程速度与距离采用线性关系导致减速过程不平稳问题,提出将速度与距离关系按照二次函数进行映射的优化方案。现场验证表明:优化后的速度曲线更加平滑,减速过程更加连贯平稳。

Energy Saving for Automatic Train Control in Moving Block Signaling System

With rapid development of the railway traffic, the moving block signaling system （MBS） method has become more and more important for increasing the track capacity by allowing trains to run in a shorter time-headway while maintaining the required safety margins. In this framework, the tracking target point of the following train is moving forward with its leading train. This paper focuses on the energy saving tracking control of two successive trains in MBS. Nonlinear programming method is used to optimize the energy-saving speed trajectory of the following train. The real-time location of the leading train could be integrated into the optimization process. Due to simplicity, it can be used for online implementation. The feasibility and effectiveness are verified through simulation. The results show that the new method is efficient on energy saving even when disturbances present.

基于CTCS2+ATO的市域铁路列车追踪间隔时间研究

基于CTCS2+ATO的市域铁路,综合考虑列车长度、运行速度、列车类型、道岔限速、到发线布置、信号系统、车载ATP控车模型、车站作业时间等因素影响,对列车区间追踪间隔、车站追踪间隔和折返追踪间隔进行研究计算,为系统设计能力指标、相关工程行车组织设计和列车运行图编制提供理论研究支撑。

南京地铁6号线ATO+方案研究

为推进南京智慧城轨的建设发展,提高列车运行智能化程度,在南京地铁6号线建设时期提出设计变更,将既有机场线与宁溧线同步进行改造:提出ATO+方案,即在既有系统功能基础上增加ATO+驾驶模式,以实现部分自动功能;参照全自动运行模式,提出ATO+方案的8个运营场景。该方案涉及既有线信号系统、车辆系统、站台门系统的改造:信号系统改造车载硬件和软件,增加ATO+驾驶模式,轨旁信号系统升级软件兼容不同配置功能的列车混合运行;车辆系统改造与信号系统的接口电路,增加列车控制线,实现ATO+驾驶模式下的列车控制;站台门系统增加站台门与车门间防夹探测功能。具体实施兼顾改造前、后列车的混合运行,以保证改造过渡期不影响列车的正常运营。ATO+方案的实施,可以大大减少司机的干预,提升行车自动化程度和运营效率,为后续类似线路建设和既有线升级改造提供参考。

城市轨道交通线路列车ATO运行曲线分析及整改措施

城市轨道交通作为一种准时速达、舒适安全的大运量交通方式,不仅能够高效实现旅客的中转距离周转,还能切实满足城市日常的交通运输需求,这一特点使得城市轨道交通在科技的作用下越发趋向于全自动化发展。但在运行的过程中仍然存有一定的问题,并从列车运行曲线中得以显现。基于此,本文以此为要点对城市轨道交通线路列车ATO运行曲线进行深度分析,并结合实际问题制定有效的整改措施,以此来提高城市轨道交通线路列车自动化运行的安全性与稳定性。

Research on Multi-Objective Real-Time Optimization of Automatic Train Operation(ATO) in Urban Rail Transit

The determination and optimization of Automatic Train Operation(ATO) control strategy is one of the most critical technologies for urban rail train operation. The practical ATO optimal control strategy must consider many goals of the train operation, such as safety, accuracy, comfort, energy saving and so on. This paper designs a set of efficient and universal multi-objective control strategy. Firstly, based on the analysis of urban rail transit and its operating environment, the multi-objective optimization model considering all the indexes of train operation is established by using multi-objective optimization theory. Secondly, Non-dominated Sorting Genetic Algorithm II(NSGA-II) is used to solve the model, and the optimal speed curve of train running is generated.Finally, the intelligent controller is designed by the combination of fuzzy controller algorithm and the predictive control algorithm, which can control and optimize the train operation in real time. Then the robustness of the control system can ensure and the requirements for multi-objective in train operation can be satisfied.

Research on Optimization of Freight Train ATO Based on Elite Competition Multi-Objective Particle Swarm Optimization

In view of the complex problems that freight train ATO (automatic train operation) needs to comprehensively consider punctuality, energy saving and safety, a dynamics model of the freight train operation process is established based on the safety and the freight train dynamics model in the process of its operation. The algorithm of combining elite competition strategy with multi-objective particle swarm optimization technology is introduced, and the winning particles are obtained through the competition between two elite particles to guide the update of other particles, so as to balance the convergence and distribution of multi-objective particle swarm optimization. The performance comparison experimental results verify the superiority of the proposed algorithm. The simulation experiments of the actual line verify the feasibility of the model and the effectiveness of the proposed algorithm.

基于CTCS2+ATO的市域铁路LTE移动通信业务承载解决方案研究

对于部分市域铁路信号系统采用CTCS2+ATO制式、移动通信系统采用LTE制式的全新组合模式,有必要根据其车地无线通信业务需求,分析研究LTE集群调度通信实现方式、系统架构,以及实现调度命令、ATO信息、无线车次号校核信息传送等业务功能的解决方案。通过分析LTE系统集群调度通信的主流实现方式,根据市域铁路车地无线通信主要业务需求,对于正在建设的市域铁路推荐采用条件相对成熟的B-TrunC架构LTE系统。重点研究基于BTrunC架构的LTE系统实现行车相关调度通信业务的工作流程,市域铁路列车信号车载设备在自动折返情况下通信车载台同步换端、车次号与IP地址转换的解决方案,对此类型的市域铁路工程建设、列控系统车地安全信息传送和运营维护方面具有指导作用。

基于CTCS2+ATO的市域(郊)铁路列控系统技术研究

介绍CTCS+ATO列控系统的功能和结构,分析并提出CTCS2+ATO列控系统应用于市域(郊)铁路列控系统需进行的优化项,分别对减少追踪间隔时间、缩短到发线有效长、支持“四网融合”、支持多模车地通信进行分析和探讨,并结合既有研究成果提出建议解决方案。

深惠城际铁路CTCS2+ATO列控系统自动折返方案研究

基于目前粤港澳大湾区城际铁路建设规划及深惠城际铁路基本情况,梳理目前城市轨道交通以及城际铁路2种轨道交通制式下列车折返模式,调研装备CTCS2+ATO列控系统的城际铁路的情况,发现列车在车站进行折返作业一般由人工完成,存在耗时长、效率低、操作繁琐等问题。通过分析深惠城际铁路的实际需求,从车辆和设备改造、优化作业流程等角度,提出具体的CTCS2+ATO列控系统自动折返实施方案设计,方案主要是对动车组、车载设备、调度集中(CTC)、列控中心(TCC)、通信控制服务器(CCS)等进行一定的修改,并设计一套折返作业流程。研究设计的自动折返方案旨在实际工程项目中得到应用,以提高列车折返作业效率,增加动车组运用周转率,实现运营效率的提升。

高速铁路车载ATO工作模式

高速铁路列车自动驾驶(ATO)系统的标准性技术文件中,主要描述了列车超速防护(ATP)系统新增工作模式和系统使用操作,缺乏独立的ATO规范要素,如车载ATO工作模式、转换条件、功能边界等,容易导致标准条款在执行过程中一致性差、内容遗漏的问题,不利于系统互联互通及优化提升。为了完善标准体系中车载ATO功能规格,借鉴欧洲列车控制系统(ETCS)体系下ATO系统需求规范的描述方法和特点,研究和设计了满足中国列车控制系统(CTCS)要求的车载ATO工作模式,并以此为中心串联起状态、功能和流程等完整技术要求,最后从运营场景出发检验了设计的准确性和可行性,对产品研发、技术演进等方面具有重要参考意义。

具有未知时变参数城轨列车的精准停车鲁棒控制

列车运行情况复杂多变,使得列车质量、基本运行阻力及线路状况等参数难以精确获得且随着列车运行存在明显的时变特性。列车模型参数的不确定性和时变特性会对列车的精准停车性能产生重要的影响。在动车组多质点动力学模型的基础上,根据Lyapunov再设计方法设计了一种非线性列车鲁棒控制器。该控制器将系统中的不确定性因素视为等价干扰,并设计相应的鲁棒补偿项。根据参数的先验区间,设计的鲁棒补偿项能够抑制模型估计误差,使得列车的位置和速度跟踪误差快速收敛,最终实现精准停车的控制目标;同时,也证明了系统的最终一致有界稳定性。最后,选取城市轨道交通场景进行仿真验证,仿真结果表明:本文提出的控制方法在列车模型参数未知且时变情景下也能实现对期望停车曲线的快速、精确追踪,表现出较强的鲁棒特性。

城轨列车自动驾驶积分反步线性自抗扰控制

针对列车在外部干扰和不确定动态下的速度控制问题,设计了融合积分反步法和线性自抗扰的复合控制方案.首先,考虑列车具有强耦合性,为了更加符合列车真实的纵向动力学特性和受力情况,建立了具有时变系数的多质点模型.其次,为了降低参数调节的难度,跟踪微分器和扩张状态观测器均采用线性形式.跟踪微分器用于求取微分信号,同时具有滤波作用.利用跟踪微分器对虚拟控制量进行求导,正好可解决反步法中存在的“微分爆炸”问题.扩张状态观测器用于实时估计总和扰动.此外,利用积分反步法改进了误差反馈控制律,设计了一种积分反步线性自抗扰控制(IBS-LADRC)算法.最后,证明了观测误差的收敛性及闭环系统稳定性.结合杭州地铁6号线AH型动车组参数和实际线路数据进行仿真,并将IBSLADRC与反步法、线性自抗扰算法、PID控制进行对比,结果表明:IBS-LADRC方法下各动力单元速度误差均处在±0.04 km/h以内,加速度处在±1 m/s2以内,加速度和速度误差均变化较平稳;车钩力相对其他3类方法最小,变化也最平缓,最大车钩力仅为2 320 N;本文控制策略对列车期望速度具有较高的跟踪精度,有利于保证车钩安全,防止车钩断裂,并提高列车运行的安全性、平稳性及乘客舒适度.

基于自适应终端滑模的高速列车迭代学习速度控制

针对高速列车的速度追踪控制问题,充分利用列车运行的重复性,考虑工程应用中迭代初始状态不同和复杂的外部环境,提出一种基于线性扩张状态观测器(LESO)的自适应非奇异终端滑模迭代学习控制算法,使系统在任意迭代初值时均能保证追踪精度。提出一种时变非奇异终端滑模面以抑制初态误差影响,采用LESO估计并补偿列车扰动,设计自适应迭代更新律估计LESO的观测误差,设计全饱和自适应迭代控制律计算输入并将其约束于允许范围内。建立类Lyapunov的复合能量函数,通过严格的数学分析证明其迭代域的差分负定性和有界性,证明所设计的时变滑模面可实现渐进收敛,并证明追踪误差在滑模面内可在有限时间内收敛至平衡点。将本文提出的算法与滑模控制、变增益迭代学习控制、自抗扰控制等算法进行比较。仿真结果表明:无论是否存在迭代初始误差,在相同的条件下,本文提出的算法较其他算法具有更强的抗干扰能力,速度追踪精度提高90%及以上,停车误差可迭代收敛至001 m。

基于ASP-SAC算法的列车自动驾驶速度控制

随着经济建设的绿色转型以及人工智能的快速发展,城市轨道交通已成为居民日常出行的重要方式,在保障安全性、高效性和准点性的前提下,列车运行的节能性和舒适性需求也越来越被关注。合理的运行策略能够有效实现多种目标需求下的列车自动驾驶速度控制,强化学习作为一种智能决策方法,能够有效解决这一控制问题。首先,通过综合分析技术、安全性和乘客体验等方面的因素,基于专家经验动作划分和状态信息熵将软演员-评论家(SAC)改进为动作状态经验优先软演员-评论家(ASP-SAC)方法,用于研究列车自动驾驶速度控制问题。其次,将问题马尔可夫形式化,搭建了列车运行环境,确定了状态空间、动作空间以及基于目标控制的奖励函数。最后,以北京地铁亦庄线的一段区间数据为例进行试验,对ASP-SAC方法进行验证并与其他一些算法在相同环境下进行性能优劣比较。研究结果表明:该方法对于多目标控制需求下的列车自动驾驶速度控制问题具有可行性,与未改进前相比算法效率提高22.73%,与PPO算法相比提高29.17%,改进效果良好。同时,列车运行时在安全性、舒适性无误的情况下,准时性、精确性和节能性都强于SAC、DQN、PPO以及PID算法,其中能耗分别减少3.64%、5.62%、4.38%、7.35%,控制效果良好。此外,该方法亦具备鲁棒性,在列车自动驾驶速度控制方面具有一定的优越性和可参考性。