Article(id=1309639678135989216, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639664567415657, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20250007, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1737561600000, revisedDateStr=2025-01-23, acceptedDate=null, acceptedDateStr=null, onlineDate=1790172031196, onlineDateStr=2026-09-23, pubDate=1774281600000, pubDateStr=2026-03-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790172031196, onlineIssueDateStr=2026-09-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790172031196, creator=13701087609, updateTime=1790172031196, updator=13701087609, issue=Issue{id=1309639664567415657, tenantId=1146029695717560320, journalId=1189621681917173762, year='2026', volume='', issue='3', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='1774281600000', pubDateStr='2026-03-24', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1790172027961, creator='13701087609', updateTime=1790232938103, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1309895140290945772, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639664567415657, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1309895140290945773, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1309639664567415657, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=18, endPage=27, ext={EN=ArticleExt(id=1309639678345704417, articleId=1309639678135989216, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Fault-Tolerant Trajectory Tracking Control for Tri-Axial Vehicles Based on Infinite-Horizon MPC, columnId=1309639665217532778, journalTitle=Automobile Technology, columnName=Special Topic on Dynamic Trajectory Prediction, Planning, and Tracking Control Technology of Intelligent Vehicle, runingTitle=null, highlight=null, articleAbstract=

To address the increased fault probability caused by the growing number of actuators in all-wheel-steering vehicles, this study focuses on motion control of a 3×3 all-wheel-steering intelligent vehicle and proposes an infinite-horizon Model Predictive Control (MPC) method that accounts for actuator input delay and faults. The algorithm decomposes the delay-aware optimization into an MPC solved over stages 0~N-1 and an LQR solved over N~ ∞ , and introduces Cholesky decomposition in the MPC stage to compute control inputs, avoiding factorization of large Karush-Kuhn-Tucker (KKT) matrices. Simulation and experimental results show that the proposed control strategy effectively achieves fault-tolerant control when actuator faults occur, while offering higher stability and control accuracy compared to traditional LQR and MPC methods.

, authors=Zhengcai Yang¹, Zixuan Yang¹, Linhe Ge¹, Junwu Zhao², Huiquan Zhang¹, authorsList=Zhengcai Yang, Zixuan Yang, Linhe Ge, Junwu Zhao, Huiquan Zhang, authorCompany=null, correspAuthors=Linhe Ge, 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=1309639678605751266, articleId=1309639678135989216, tenantId=1146029695717560320, journalId=1189621681917173762, language=CN, title=基于无限时域模型预测控制的三轴车辆可容错轨迹跟踪控制, columnId=1309639665360139116, journalTitle=汽车技术, columnName=智能车辆动态轨迹预测、规划与跟踪控制技术专题, runingTitle=null, highlight=null, articleAbstract=

为解决全轮转向车辆因执行机构数量的增多导致故障概率增加的问题，以3×3全轮转向智能车辆的运动控制为研究对象，提出了一种考虑执行器输入延迟和故障的无限时域模型预测控制方法。该算法将考虑执行器输入延时的优化问题拆分为0~N-1阶段的模型预测控制（MPC）求解，以及N~∞阶段的线性二次型调节器（LQR）求解，并在MPC问题中引入楚列斯基（Cholesky）分解以求解控制量，避免了大规模卡罗需-库恩-塔克（KKT）矩阵的分解运算。仿真与试验结果表明：所提出的控制策略在执行机构发生故障时进行了有效的容错控制，同时与传统LQR和MPC相比具有更高的稳定性和控制精度。

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