Article(id=1319244971476103267, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1319244966216446015, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20260302, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1784044800000, revisedDateStr=2026-07-15, acceptedDate=null, acceptedDateStr=null, onlineDate=1792462111586, onlineDateStr=2026-10-20, pubDate=1790179200000, pubDateStr=2026-09-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792462111586, onlineIssueDateStr=2026-10-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792462111586, creator=13701087609, updateTime=1792462111586, updator=13701087609, issue=Issue{id=1319244966216446015, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='9', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1790179200000', pubDateStr='2026-09-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792462110332, creator='13701087609', updateTime=1792735854454, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1320393132613681559, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1319244966216446015, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1320393132613681560, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1319244966216446015, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=19, endPage=27, ext={EN=ArticleExt(id=1319244971765510244, articleId=1319244971476103267, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Research on Path Stability Tracking Control for Multi-Axle Steering Vehicles Based on Dynamic Adjustment of Control Weighting, columnId=1319244967055306816, journalTitle=Automobile Technology, columnName=Special Topic on Lateral Stability and Motion Tracking Control Technology for Steer-by-Wire Vehicles, runingTitle=null, highlight=null, articleAbstract=

To address the difficulty in coordinating path tracking accuracy and lateral stability of multi-axle steering vehicles under complex conditions such as high-speed driving and low-road adhesion, this paper proposes a path-stable tracking control method that integrates fuzzy control with integral sliding mode control. The proposed method calculates the front-wheel steering angle based on a driver preview model to track the desired path, and obtains the desired yaw rate and desired sideslip angle through a linear two-degree-of-freedom vehicle model. In the controller design, the yaw rate error and sideslip angle error are weighted and combined into a composite control error, based on which an integral sliding mode controller is constructed. Active rear-axle steering is then used to coordinate path tracking performance and vehicle stability. A fuzzy controller is further introduced to adjust the weighting coefficient online according to vehicle state errors, thereby improving the adaptability of the controller under different driving conditions. Simulink-TruckSim co-simulation results show that, under the high-speed and high-adhesion condition, compared with the conventional sliding mode control method, the proposed method reduces the maximum, average, and root mean square path tracking errors by approximately 26.5%, 43.3%, and 28.3%, respectively. Under the medium-speed and low-adhesion condition, the average and root mean square errors are reduced by approximately 24.2% and 3.8%, respectively. A low-speed double-lane-change real-vehicle test further verifies the feasibility and path tracking effectiveness of the proposed method.

, authors=Ao Lü¹, Junnan Huang^{2, 3}, Hui Huang^{2, 3}, authorsList=Ao Lü, Junnan Huang, Hui Huang, 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=1319244972054917221, articleId=1319244971476103267, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于控制权重动态调整的多轴转向车辆路径稳定跟踪控制研究, columnId=1319244967223078978, journalTitle=汽车技术, columnName=线控转向车辆横向稳定性与运动跟踪控制技术专题, runingTitle=null, highlight=null, articleAbstract=

针对在高速、低附着等复杂工况下，多轴转向车辆路径跟踪精度与横向稳定性难以协调的问题，提出一种融合模糊控制与积分滑模控制的路径稳定跟踪控制方法。基于驾驶员预瞄模型计算前轮转角，实现期望路径跟踪，并利用线性二自由度车辆模型获取期望横摆角速度和期望质心侧偏角；在控制器设计中，将横摆角速度偏差与质心侧偏角偏差加权融合为综合控制偏差，构建积分滑模控制器，并通过主动后轴转向协调路径跟踪性能与车辆稳定性；进一步引入模糊控制器，根据车辆状态偏差在线调整权重系数，提高控制器对不同工况的适应能力。Simulink-TruckSim联合仿真结果表明：相较于传统滑模控制，在高速高附着工况下，所提出方法使路径跟踪最大误差、平均误差和均方根误差分别降低约26.5%、43.3%和28.3%；在中速低附着工况下，平均误差和均方根误差分别降低约24.2%和3.8%。同时，经过实车低速双移线工况试验进一步验证了该方法的可实现性与路径跟踪有效性。

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