Article(id=1308463080443240869, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308463078174122404, articleNumber=null, orderNo=null, doi=10.20104/j.cnki.1674-6546.20250002, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=1736092800000, revisedDateStr=2025-01-06, acceptedDate=null, acceptedDateStr=null, onlineDate=1789891508446, onlineDateStr=2026-09-20, pubDate=1773504000000, pubDateStr=2026-03-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1789891508446, onlineIssueDateStr=2026-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1789891508446, creator=13701087609, updateTime=1789891508446, updator=13701087609, issue=Issue{id=1308463078174122404, tenantId=1146029695717560320, journalId=1189918454225211397, year='2026', volume='', issue='3', pageStart='1', pageEnd='48', issueExtLink='null', onlineDate='null', pubDate='1773504000000', pubDateStr='2026-03-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1789891507904, creator='13701087609', updateTime=1789892812753, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1308468551250105303, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308463078174122404, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1308468551250105304, tenantId=1146029695717560320, journalId=1189918454225211397, issueId=1308463078174122404, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1, endPage=6, ext={EN=ArticleExt(id=1308463080602624422, articleId=1308463080443240869, tenantId=1146029695717560320, journalId=1189918454225211397, language=EN, title=LQR Control Strategy Research for Active Suspension Based on Red-Billed Blue Magpie, columnId=null, journalTitle=Automotive Engineer, columnName=null, runingTitle=null, highlight=null, articleAbstract=

The weighting coefficient matrix in the Linear Quadratic Regulator (LQR) of the active suspension system for vehicles has drawbacks of subjectivity and low efficiency. To address this issue, the paper proposes a weight coefficient matrix strategy based on the Red-billed Blue Magpie optimization algorithm. A 1/4 vehicle model, random road surface and impact road surface model are established. A linear quadratic regulator is designed, and a fitness function is constructed. The optimal weighting coefficient matrix and the minimum fitness function value are obtained through the Red-billed Blue Magpie algorithm in the search space. MATLAB/Simulink is used for simulation and a comparative analysis is conducted on passive suspension systems and active suspension systems under C-class road and impact road excitations, along with other optimization algorithms. The simulation results indicate that this proposed control strategy achieves optimizations of 75.50% for the vehicle body vertical acceleration, 48.23% for the wheel dynamic load and 47.63% for the suspension dynamic travel on Class-C road surface compared with the passive suspension and snake algorithm optimization active suspension control. The first peak value of vertical acceleration on the impact road surface is optimized by 76.80%, enhancing vehicle ride comfort and safety.

, authors=Jinyun Lü, Yuying Qin, Junfeng Du, authorsList=Jinyun Lü, Yuying Qin, Junfeng Du, 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=1308463080820728231, articleId=1308463080443240869, tenantId=1146029695717560320, journalId=1189918454225211397, language=CN, title=基于红嘴蓝鹊线性二次型调节器的主动悬架控制策略研究, columnId=0, journalTitle=汽车工程师, columnName=, runingTitle=null, highlight=null, articleAbstract=

针对车辆主动悬架系统的线性二次型调节器（LQR）权重系数矩阵设定过程具有主观性、效率低的问题，提出一种基于红嘴蓝鹊优化算法的权重系数矩阵设定策略。建立了1/4车辆模型、随机路面和冲击路面模型，并完成线性二次型调节器设计和适应度函数构建，通过红嘴蓝鹊算法在搜索空间中求解最优权重系数矩阵和最小适应度函数值，最后利用MATLAB/Simulink，分别以C级路面、冲击路面为激励，与被动悬架和采用其他优化算法时主动悬架控制效果进行对比仿真分析。结果表明：与被动悬架、蛇算法优化主动悬架控制结果相比，所提出的控制策略在C级路面上对车身垂向加速度、车轮动载荷及悬架动行程的优化分别达到75.50%、48.23%、47.63%和62.07%、22.56%、21.16%，在冲击路面上使车身垂向加速度第一峰值分别降低76.80%、3.13%，提升了车辆的行驶平顺性和安全性。

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