Article(id=1308463026726789399, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308463017016972142, articleNumber=null, orderNo=null, doi=10.20104/j.cnki.1674-6546.20250284, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1764864000000, revisedDateStr=2025-12-05, acceptedDate=null, acceptedDateStr=null, onlineDate=1789891495638, onlineDateStr=2026-09-20, pubDate=1778774400000, pubDateStr=2026-05-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789891495638, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789891495638, creator=13701087609, updateTime=1789891495638, updator=13701087609, issue=Issue{id=1308463017016972142, tenantId=1146029695717560320, journalId=1189918454225211397, year='2026', volume='', issue='5', pageStart='1', pageEnd='48', issueExtLink='null', onlineDate='null', pubDate='1778774400000', pubDateStr='2026-05-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789891493324, creator='13701087609', updateTime=1789892866211, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308468775385322487, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308463017016972142, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308468775385322488, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308463017016972142, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=34, endPage=41, ext={EN=ArticleExt(id=1308463026881978648, articleId=1308463026726789399, tenantId=1146029695717560320, journalId=1189918454225211397, language=EN, title=Coordinated Control of Trajectory Tracking and Yaw Stability for Intelligent Vehicles Considering Model Uncertainty, columnId=null, journalTitle=Automotive Engineer, columnName=null, runingTitle=null, highlight=null, articleAbstract=

To improve the accuracy and stability of trajectory tracking control for intelligent vehicle, the paper presents an extension coordinated control approach that simultaneously improves tracking performance and maintains yaw stability in the presence of model uncertainty. In the upper layer, the real-time vehicle driving state is mapped to the extension set based on extension theory, where the control mode and weight distribution are determined according to the correlation function. The lower layer considers modeling errors and utilizes a trajectory tracking controller that integrates an Extended State Observer (ESO) with Model Predictive Control (MPC) to compute the front wheel steering angle control input. The Active Disturbance Rejection Control (ADRC) theory is employed to formulate a Direct Yaw Moment Controller (DYC), calculating the corresponding wheel braking torque according to the desired yaw rate. Simulation results validate that the improved tracking performance and stability using the proposed approach.

, authors=Maojie Zhao1, 2, Bin Lu1, 2, Bing He1, 2, Deng Pan1, 2, Yingbo Li1, 2, Lipeng Cao1, 2, authorsList=Maojie Zhao, Bin Lu, Bing He, Deng Pan, Yingbo Li, Lipeng Cao, authorCompany=null, correspAuthors=null, 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=1308463027108471065, articleId=1308463026726789399, tenantId=1146029695717560320, journalId=1189918454225211397, language=CN, title=考虑模型不确定性的智能汽车轨迹跟踪与横摆稳定性协调控制, columnId=0, journalTitle=汽车工程师, columnName=, runingTitle=null, highlight=null, articleAbstract=

为提高智能汽车轨迹跟踪控制的精度和稳定性,提出了考虑模型不确定性的车辆轨迹跟踪与横摆稳定性可拓协调控制方法。上层基于可拓理论,将车辆行驶实时状态映射至可拓集合,根据关联函数确定控制模式和权重分配;下层考虑建模误差,采用轨迹跟踪控制器结合扩张状态观测器(ESO)和模型预测控制(MPC)算法求解前轮转角控制量,并基于自抗扰控制(ADRC)理论建立直接横摆力矩控制器,根据期望横摆角速度计算相应车轮制动力矩。仿真结果表明,该方法使横向误差均方根值和最大质心侧偏角分别减小了40%和45%,有效提高了跟踪精度和稳定性。

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考虑模型不确定性的智能汽车轨迹跟踪与横摆稳定性协调控制
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赵茂杰 1, 2 , 路斌 1, 2 , 何兵 1, 2 , 潘登 1, 2 , 李影博 1, 2 , 曹礼鹏 1, 2
汽车工程师 | 2026,(5): 34-41
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汽车工程师 | 2026 , (5) : 34 -41
考虑模型不确定性的智能汽车轨迹跟踪与横摆稳定性协调控制
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赵茂杰1, 2, 路斌1, 2, 何兵1, 2, 潘登1, 2, 李影博1, 2, 曹礼鹏1, 2
作者信息
  • 1.中国汽车工程研究院股份有限公司,重庆 401100
  • 2.中汽院(江苏)汽车工程研究院有限公司,苏州 215129
Coordinated Control of Trajectory Tracking and Yaw Stability for Intelligent Vehicles Considering Model Uncertainty
Maojie Zhao1, 2, Bin Lu1, 2, Bing He1, 2, Deng Pan1, 2, Yingbo Li1, 2, Lipeng Cao1, 2
Affiliations
  • 1.China Automotive Engineering Research Institute Co., Ltd., Chongqing 401100
  • 2.Jiangsu CAERI Automotive Engineering Research Institute Co., Ltd., Suzhou 215129
出版时间: 2026-05-15 doi: 10.20104/j.cnki.1674-6546.20250284
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为提高智能汽车轨迹跟踪控制的精度和稳定性,提出了考虑模型不确定性的车辆轨迹跟踪与横摆稳定性可拓协调控制方法。上层基于可拓理论,将车辆行驶实时状态映射至可拓集合,根据关联函数确定控制模式和权重分配;下层考虑建模误差,采用轨迹跟踪控制器结合扩张状态观测器(ESO)和模型预测控制(MPC)算法求解前轮转角控制量,并基于自抗扰控制(ADRC)理论建立直接横摆力矩控制器,根据期望横摆角速度计算相应车轮制动力矩。仿真结果表明,该方法使横向误差均方根值和最大质心侧偏角分别减小了40%和45%,有效提高了跟踪精度和稳定性。

模型不确定  /  轨迹跟踪  /  横摆稳定性  /  可拓协调控制

To improve the accuracy and stability of trajectory tracking control for intelligent vehicle, the paper presents an extension coordinated control approach that simultaneously improves tracking performance and maintains yaw stability in the presence of model uncertainty. In the upper layer, the real-time vehicle driving state is mapped to the extension set based on extension theory, where the control mode and weight distribution are determined according to the correlation function. The lower layer considers modeling errors and utilizes a trajectory tracking controller that integrates an Extended State Observer (ESO) with Model Predictive Control (MPC) to compute the front wheel steering angle control input. The Active Disturbance Rejection Control (ADRC) theory is employed to formulate a Direct Yaw Moment Controller (DYC), calculating the corresponding wheel braking torque according to the desired yaw rate. Simulation results validate that the improved tracking performance and stability using the proposed approach.

Model uncertainty  /  Trajectory tracking  /  Yaw stability  /  Extension coordinated control
赵茂杰, 路斌, 何兵, 潘登, 李影博, 曹礼鹏. 考虑模型不确定性的智能汽车轨迹跟踪与横摆稳定性协调控制. 汽车工程师, 2026 , (5) : 34 -41 . DOI: 10.20104/j.cnki.1674-6546.20250284
Maojie Zhao, Bin Lu, Bing He, Deng Pan, Yingbo Li, Lipeng Cao. Coordinated Control of Trajectory Tracking and Yaw Stability for Intelligent Vehicles Considering Model Uncertainty[J]. Automotive Engineer, 2026 , (5) : 34 -41 . DOI: 10.20104/j.cnki.1674-6546.20250284
  • 重庆市自然科学基金项目(CSTB2025NSCQ-BSX0004)
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doi: 10.20104/j.cnki.1674-6546.20250284
  • 首发时间:2026-09-20
  • 出版时间:2026-05-15
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  • 修回日期:2025-12-05
基金
重庆市自然科学基金项目(CSTB2025NSCQ-BSX0004)
作者信息
    1.中国汽车工程研究院股份有限公司,重庆 401100
    2.中汽院(江苏)汽车工程研究院有限公司,苏州 215129
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https://castjournals.cast.org.cn/joweb/qcgcs/CN/10.20104/j.cnki.1674-6546.20250284
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2种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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