Article(id=1308462991364612195, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308462989946934053, articleNumber=null, orderNo=null, doi=10.20104/j.cnki.1674-6546.20250263, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1765987200000, revisedDateStr=2025-12-18, acceptedDate=null, acceptedDateStr=null, onlineDate=1789891487208, onlineDateStr=2026-09-20, pubDate=1781452800000, pubDateStr=2026-06-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789891487208, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789891487208, creator=13701087609, updateTime=1789891487208, updator=13701087609, issue=Issue{id=1308462989946934053, tenantId=1146029695717560320, journalId=1189918454225211397, year='2026', volume='', issue='6', pageStart='1', pageEnd='48', issueExtLink='null', onlineDate='null', pubDate='1781452800000', pubDateStr='2026-06-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789891486870, creator='13701087609', updateTime=1789892888486, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308468868809248787, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308462989946934053, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308468868809248788, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308462989946934053, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=23, endPage=30, ext={EN=ArticleExt(id=1308462991633047652, articleId=1308462991364612195, tenantId=1146029695717560320, journalId=1189918454225211397, language=EN, title=Variable-Weighting Linear Quadratic Regulator Control Strategy for Active Suspension Systems, columnId=null, journalTitle=Automotive Engineer, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Traditional Linear Quadratic Regulator (LQR) control in vehicle active suspension systems faces challenges in precisely directing optimization due to fixed weight coefficients, which fail to meet the demands of complex operating conditions. To address this issue, a permanent magnet linear motor actuator model, a seven-degree-of-freedom active suspension model and state-space equations are established. Multiple operating modes are defined according to different road profiles and vehicle speeds. The Analytic Hierarchy Process (AHP) is employed to determine weighted coefficients for the fitness function, and the Red-billed Blue Magpie Optimization (RBMO) algorithm is utilized to optimize the LQR weight coefficient matrices. Simulations under various operating conditions are conducted using MATLAB/Simulink. The results indicate that, compared with a passive suspension system, the proposed strategy improves vertical body acceleration, pitch angular acceleration and the dynamic deflection of the right front, left front, left rear, and right rear suspensions in comfort mode by 27%, 30%, 17%, 14%, 8%, and 15%, respectively. In safety mode, roll angular acceleration and the dynamic loads of the right front, left front, left rear, and right rear tires are improved by 41%, 15%, 18%, 15%, and 17%, respectively. In integrated mode, body acceleration pitch angular acceleration and roll angular acceleration are improved by 35%, 32%, and 48%, respectively. Further validation through CarSim/Simulink co-simulation demonstrates that the proposed control strategy achieves a notable level of performance improvement across different modes.

, authors=Jinyun Lü, Yuying Qin, Junfeng Du, Jun Wang, authorsList=Jinyun Lü, Yuying Qin, Junfeng Du, Jun Wang, 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=1308462991846957157, articleId=1308462991364612195, tenantId=1146029695717560320, journalId=1189918454225211397, language=CN, title=基于变权重线性二次型调节器的主动悬架控制, columnId=0, journalTitle=汽车工程师, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对车辆主动悬架系统线性二次型调节器（LQR）因固定权重系数无法满足复杂工况需求，导致优化方向难以精准控制的问题，建立永磁直线电机作动器模型、七自由度主动悬架模型和状态空间方程，根据不同路面、车速划分多种工作模式，使用层次分析法（AHP）确定适应度函数加权系数，利用红嘴蓝鹊优化（RBMO）算法优化权重系数矩阵，在不同工况下进行MATLAB/Simulink仿真分析，结果表明：与被动悬架相比，舒适性模式下车身垂向加速度、俯仰角加速度，以及右前、左前、左后、右后侧悬架动行程分别改善27%、30%、17%、14%、8%、15%；安全性模式下侧倾角加速度及右前、左前、左后、右后侧轮胎动载荷分别改善41%、15%、18%、15%、17%；综合性模式下车身垂向加速度、俯仰角加速度、侧倾角加速度分别改善35%、32%、48%。进一步通过CarSim/Simulink联合仿真分析所提出策略的综合性能，结果表明，该控制策略在不同模式下取得了一定程度的改善。

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