Article(id=1308454764853879145, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308454712513156008, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1672-6073.2026.04.016, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1766419200000, receivedDateStr=2025-12-23, revisedDate=1773244800000, revisedDateStr=2026-03-12, acceptedDate=null, acceptedDateStr=null, onlineDate=1789889525854, onlineDateStr=2026-09-20, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789889525854, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789889525854, creator=13701087609, updateTime=1789889525854, updator=13701087609, issue=Issue{id=1308454712513156008, tenantId=1146029695717560320, journalId=1146123302524792850, year='2026', volume='39', issue='4', pageStart='1', pageEnd='197', issueExtLink='null', onlineDate='null', pubDate='1786291200000', pubDateStr='2026-08-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1789889513376, creator='13701087609', updateTime=1789889839088, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308456079029986178, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308454712513156008, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308456079029986179, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1308454712513156008, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=141, endPage=146, ext={EN=ArticleExt(id=1308454767102026091, articleId=1308454764853879145, tenantId=1146029695717560320, journalId=1146123302524792850, language=EN, title=Closed-Loop Control Method for Braking Deceleration of Urban Rail Trains Based on Multi-System Fusion, columnId=1152669335454658940, journalTitle=Urban Rapid Rail Transit, columnName=Academic Discussion, runingTitle=null, highlight=null, articleAbstract=

Against the background of the rapid development of autonomous train operation and virtual coupling, the traditional independent design of vehicle and signaling systems and the distributed control system adopted on the vehicle side have led to problems such as lengthy on-board Automatic Train Operation (ATO) control links, large response delays, and low control accuracy. This paper proposes a multi-system fusion-based closed-loop control method for train braking deceleration. Firstly, an integrated hardware and software platform integrating vehicle control and signaling control functions is established to construct a multi-system fusion control architecture. secondly, based on this platform, the functional redistribution of the entire process of train automatic operation control is carried out, and the core links such as ATO control, traction/braking force collaborative distribution, and closed-loop feedback control are deeply integrated to reconstruct the control closed-loop; Finally, on-vehicle tests are conducted to verify the control performance of the proposed method. The test results show that, compared with the traditional distributed train control system, the control accuracy under ATO mode is increased by 35% with a 25% reduction in response delay; the accuracy of pure electric braking is improved by 70% with a 20% delay reduction; and the accuracy of electro-pneumatic blended braking is raised by 30% with a 14% delay reduction. The integrated architecture effectively improves train braking stability and stopping accuracy, providing critical technical support for the safe and efficient intelligent and autonomous operation of trains.

, authors=Xisheng Xia¹, Lijun Yang¹, Hongjie Liu², Tao Tang³, Yang Li¹, Haoyang Yu¹, authorsList=Xisheng Xia, Lijun Yang, Hongjie Liu, Tao Tang, Yang Li, Haoyang Yu, authorCompany=null, correspAuthors=Hongjie Liu, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1308454768171573613, articleId=1308454764853879145, tenantId=1146029695717560320, journalId=1146123302524792850, language=CN, title=多系统融合的城轨列车制动减速度闭环控制方法, columnId=1152669335668568445, journalTitle=都市快轨交通, columnName=学术探讨, runingTitle=null, highlight=null, articleAbstract=

针对列车自主运行与虚拟编组新型运行制式快速发展背景下，传统列车车辆系统与车载信号系统独立设计、车辆侧采用分布式控制架构，导致的列车自动驾驶（automatic train operation, ATO）控车链路冗长、响应延迟大及控制精度低等工程问题，提出一种多系统融合的列车减速度闭环控制方法。首先，搭建集成车辆控制与信号控制功能的一体化软硬件平台，构建多系统融合控制架构；其次，基于该平台对列车自动驾驶控制全过程进行功能重分配，深度融合ATO控车调控、牵引/制动力协同分配、闭环反馈控制等核心环节，重构控制闭环；最后，通过实车试验对所提方法的控制性能进行验证。测试结果表明：相较于传统分布式列车控制系统，融合控制系统在多个工况下控车效果均得到提升，融合控制系统ATO控车模式控制精度提升35%，响应延时缩短25%；纯电制动工况下控制精度提升70%、响应延时缩短20%；电空混合制动工况下控制精度提升30%，响应延时缩短14%。该多系统融合控制架构可有效改善列车制动平稳性，提升停车控制精度，为列车智能化、自主化安全高效运行提供重要的技术支撑。

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夏夕盛，男，硕士，正高级工程师，从事轨道交通信号与车辆技术研发，xisheng.xia@bj-tct.com

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夏夕盛，男，硕士，正高级工程师，从事轨道交通信号与车辆技术研发，xisheng.xia@bj-tct.com

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