Article(id=1207343644726628410, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1207343627223802520, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2409185, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1733760000000, receivedDateStr=2024-12-10, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1765782756262, onlineDateStr=2025-12-15, pubDate=1750176000000, pubDateStr=2025-06-18, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1765782756262, onlineIssueDateStr=2025-12-15, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1765782756262, creator=13701087609, updateTime=1765782756262, updator=13701087609, issue=Issue{id=1207343627223802520, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='17', pageStart='7023', pageEnd='7453', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1765782752085, creator=13701087609, updateTime=1765783816840, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1207348093192872694, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1207343627223802520, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1207348093192872695, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1207343627223802520, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=7365, endPage=7372, ext={EN=ArticleExt(id=1207343648165957845, articleId=1207343644726628410, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=High-speed Lateral Stability of Double Semi-trailer Trucks Train with Coordinated Control, columnId=1207343630017208991, journalTitle=Science Technology and Engineering, columnName=Papers-Traffics and Transportations, runingTitle=null, highlight=null, articleAbstract=

The double semi-trailer truck train has strong transportation capacity and relatively low transportation costs. However, compared to semi-trailers, double semi-trailer truck train has more vehicle units, resulting in higher driving difficulty and lower lateral stability at high speeds. To address this issue, a control strategy combining model predictive control (MPC) with differential braking was proposed. Based on the principles of MPC, an MPC lateral stability controller for double semi-trailer truck trains has been designed. MPC regulates the yaw moments of three vehicle units, with differential braking technology dynamically allocating braking forces to individual wheels. The double semi-trailer truck train model has been built in Trucksim, and a simplified vehicle model has been established in MATLAB/Simulink. Through joint simulation of Trucksim and MATLAB/Simulink, the effectiveness of the designed system was verified under different vehicle speeds, loads, and friction coefficients. The research results indicate that the designed controller effectively reduces the centroid yaw angle, lateral acceleration, and yaw rate of each vehicle unit, thereby enhancing the stability of double semi-trailer truck train during high-speed lane changes.

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双半挂汽车列车具备较强的运输能力和相对较低的运输成本等优势。然而,与半挂车相比,双半挂汽车列车拥有更多的车辆单元,导致驾驶难度更高且高速横向稳定性更差。针对此问题,提出了模型预测控制(model predictive control,MPC)与差动制动相互结合的控制策略。根据MPC原理,设计了MPC双半挂汽车列车横向稳定性控制器。通过MPC控制3个车辆单元的横摆力矩,结合差动制动技术从而实现各个车轮制动力的分配。在Trucksim搭建了双半挂汽车列车模型且MATLAB/Simulink中建立车辆简化模型。通过Trucksim与MATLAB/Simulink联合仿真,在不同车速、负载、附着系数情况下验证了所设计系统的有效性。研究结果表明:所设计的控制器有效地降低了各车辆单元的质心侧偏角、侧向加速度和横摆角速度等,提高了双半挂汽车列车高速变道时的稳定性。

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周庆辉(1973—),男,汉族,山东济南人,博士,教授。研究方向:车辆智能控制技术。E-mail:

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周庆辉(1973—),男,汉族,山东济南人,博士,教授。研究方向:车辆智能控制技术。E-mail:

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周庆辉(1973—),男,汉族,山东济南人,博士,教授。研究方向:车辆智能控制技术。E-mail:

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Changchun: Jilin University, 2019., articleTitle=null, refAbstract=null)], funds=[Fund(id=1207400967583146664, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1207343644726628410, awardId=2022-K-079, language=CN, fundingSource=住房和城乡建设部科学技术计划(2022-K-079), fundOrder=null, country=null), Fund(id=1207400967708975793, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1207343644726628410, awardId=null, language=CN, fundingSource=北京建筑大学研究生创新项目(双半挂横向稳定性研究), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1207400959899181436, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1207343644726628410, xref=1, ext=[AuthorCompanyExt(id=1207400959907570044, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1207343644726628410, companyId=1207400959899181436, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1 School of Mechanical-Electrical and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China), AuthorCompanyExt(id=1207400959945318781, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1207343644726628410, companyId=1207400959899181436, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1 北京建筑大学机电与车辆工程学院, 北京 100044)]), AuthorCompany(id=1207400960062759297, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1207343644726628410, xref=2, ext=[AuthorCompanyExt(id=1207400960071147906, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1207343644726628410, companyId=1207400960062759297, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2 Beijing Engineering Research Center of Monitoring for Construction Safety, Beijing 100044, China), AuthorCompanyExt(id=1207400960079536515, 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label=Fig.2, caption=B-double truck train simplified diagram, figureFileSmall=dd5E/kqDmkZtTbqfSoTixA==, figureFileBig=96Q0FCLN6CGiJvErnYPVfg==, tableContent=null), ArticleFig(id=1207400963887964679, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1207343644726628410, language=CN, label=图2, caption=B-double汽车列车简化示意图, figureFileSmall=dd5E/kqDmkZtTbqfSoTixA==, figureFileBig=96Q0FCLN6CGiJvErnYPVfg==, tableContent=null), ArticleFig(id=1207400963988627983, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1207343644726628410, language=EN, label=Fig.3, caption=Control schematic diagram, figureFileSmall=BOikWwMZYNN0mgJ4kmlZzw==, figureFileBig=H/jN5LmMr0so8i3ZMRhuAg==, tableContent=null), ArticleFig(id=1207400964047348246, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1207343644726628410, language=CN, label=图3, caption=控制原理图, figureFileSmall=BOikWwMZYNN0mgJ4kmlZzw==, figureFileBig=H/jN5LmMr0so8i3ZMRhuAg==, tableContent=null), 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journalId=1146123166801305609, articleId=1207343644726628410, language=EN, label=Table 1, caption=

Vehicle parameters

, figureFileSmall=null, figureFileBig=null, tableContent=
名称 数值
牵引车长度/mm 6 440
一挂车长度/mm 9 060
二挂车长度/mm 12 176
总长/mm 21 974
牵引车重/t 8.4
一挂车空载/t 7.5
一挂车满载/t 25.3
二挂车空载/t 7.5
二挂车满载/t 25.3
牵引车转动惯量/(kg·m2) 43 996
一挂车空载转动惯量/(kg·m2) 107 400
一挂车满载转动惯量/(kg·m2) 760 280
二挂车空载转动惯量/(kg·m2) 107 400
二挂车满载转动惯量/(kg·m2) 760 280
), ArticleFig(id=1207400965611823732, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1207343644726628410, language=CN, label=表1, caption=

车辆参数

, figureFileSmall=null, figureFileBig=null, tableContent=
名称 数值
牵引车长度/mm 6 440
一挂车长度/mm 9 060
二挂车长度/mm 12 176
总长/mm 21 974
牵引车重/t 8.4
一挂车空载/t 7.5
一挂车满载/t 25.3
二挂车空载/t 7.5
二挂车满载/t 25.3
牵引车转动惯量/(kg·m2) 43 996
一挂车空载转动惯量/(kg·m2) 107 400
一挂车满载转动惯量/(kg·m2) 760 280
二挂车空载转动惯量/(kg·m2) 107 400
二挂车满载转动惯量/(kg·m2) 760 280
), ArticleFig(id=1207400965720875643, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1207343644726628410, language=EN, label=Table 2, caption=

Selection of target wheels

, figureFileSmall=null, figureFileBig=null, tableContent=
${r}_{i\_\mathrm{r}\mathrm{e}\mathrm{f}}$ ri ${r}_{i}-{r}_{i\_\mathrm{r}\mathrm{e}\mathrm{f}}$ Mi 制动车轮
牵引车 一挂 二挂
逆时针 + R1 R4,R5,R6 R7,R8,R9
- L2,L3 L4,L5,L6 L7,L8,L9
- L1 L4,L5,L6 L7,L8,L9
0 - L2,L3 L4,L5,L6 L7,L8,L9
顺时针 + L1 L4,L5,L6 L7,L8,L9
- R2,R3 R4,R5,R6 R7,R8,R9
+ R1 R4,R5,R6 R7,R8,R9
0 + R2,R3 R4,R5,R6 R7,R8,R9
0 - L1 L4,L5,L6 L7,L8,L9
+ R2,R3 R4,R5,R6 R7,R8,R9
), ArticleFig(id=1207400966924640897, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1207343644726628410, language=CN, label=表2, caption=

目标车轮的选择

, figureFileSmall=null, figureFileBig=null, tableContent=
${r}_{i\_\mathrm{r}\mathrm{e}\mathrm{f}}$ ri ${r}_{i}-{r}_{i\_\mathrm{r}\mathrm{e}\mathrm{f}}$ Mi 制动车轮
牵引车 一挂 二挂
逆时针 + R1 R4,R5,R6 R7,R8,R9
- L2,L3 L4,L5,L6 L7,L8,L9
- L1 L4,L5,L6 L7,L8,L9
0 - L2,L3 L4,L5,L6 L7,L8,L9
顺时针 + L1 L4,L5,L6 L7,L8,L9
- R2,R3 R4,R5,R6 R7,R8,R9
+ R1 R4,R5,R6 R7,R8,R9
0 + R2,R3 R4,R5,R6 R7,R8,R9
0 - L1 L4,L5,L6 L7,L8,L9
+ R2,R3 R4,R5,R6 R7,R8,R9
), ArticleFig(id=1207400967029498502, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1207343644726628410, language=EN, label=Table 3, caption=

Simulation results of operating condition 1

, figureFileSmall=null, figureFileBig=null, tableContent=
v=100 km/h
μ=0.85
满载 空载
无控制 有控制 无控制 有控制
ayi,max/g ay1 0.258 0.236 0.255 0.221
ay2 0.280 0.242 0.263 0.223
ay3 0.334 0.268 0.291 0.242
rimax/
[(°)·s-1]
r1 7.304 6.366 6.923 6.603
r2 7.848 6.653 7.224 6.426
r3 8.232 6.919 7.424 6.520
βimax/(°) ${\beta }_{1}$ 1.393 1.252 1.194 1.015
${\beta }_{2}$ 1.474 1.267 1.270 1.021
${\beta }_{3}$ 1.385 1.202 1.238 1.039
), ArticleFig(id=1207400967159521934, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1207343644726628410, language=CN, label=表3, caption=

工况一仿真结果

, figureFileSmall=null, figureFileBig=null, tableContent=
v=100 km/h
μ=0.85
满载 空载
无控制 有控制 无控制 有控制
ayi,max/g ay1 0.258 0.236 0.255 0.221
ay2 0.280 0.242 0.263 0.223
ay3 0.334 0.268 0.291 0.242
rimax/
[(°)·s-1]
r1 7.304 6.366 6.923 6.603
r2 7.848 6.653 7.224 6.426
r3 8.232 6.919 7.424 6.520
βimax/(°) ${\beta }_{1}$ 1.393 1.252 1.194 1.015
${\beta }_{2}$ 1.474 1.267 1.270 1.021
${\beta }_{3}$ 1.385 1.202 1.238 1.039
), ArticleFig(id=1207400967264379541, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1207343644726628410, language=EN, label=Table 4, caption=

Simulation results of operating condition 2

, figureFileSmall=null, figureFileBig=null, tableContent=
v=88 km/h
μ=0.5
满载 空载
无控制 有控制 无控制 有控制
${{a}_{yi,}}_{\mathrm{m}\mathrm{a}\mathrm{x}}$/g ay1 0.213 0.196 0.203 0.180
ay2 0.223 0.192 0.208 0.183
ay3 0.244 0.209 0.217 0.189
rimax/
[(°)·s-1]
r1 6.513 5.676 6.019 5.814
r2 6.553 5.668 6.003 5.446
r3 6.596 5.663 6.083 5.378
βimax/(°) β1 1.086 0.954 0.881 0.739
β2 1.001 0.853 0.888 0.717
β3 0.829 0.714 0.785 0.638
), ArticleFig(id=1207400967369237147, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1207343644726628410, language=CN, label=表4, caption=

工况二仿真结果

, figureFileSmall=null, figureFileBig=null, tableContent=
v=88 km/h
μ=0.5
满载 空载
无控制 有控制 无控制 有控制
${{a}_{yi,}}_{\mathrm{m}\mathrm{a}\mathrm{x}}$/g ay1 0.213 0.196 0.203 0.180
ay2 0.223 0.192 0.208 0.183
ay3 0.244 0.209 0.217 0.189
rimax/
[(°)·s-1]
r1 6.513 5.676 6.019 5.814
r2 6.553 5.668 6.003 5.446
r3 6.596 5.663 6.083 5.378
βimax/(°) β1 1.086 0.954 0.881 0.739
β2 1.001 0.853 0.888 0.717
β3 0.829 0.714 0.785 0.638
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双半挂汽车列车联合控制下高速横向稳定性
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周庆辉 1, 2 , 张博宇 1
科学技术与工程 | 论文·交通运输 2025,25(17): 7365-7372
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科学技术与工程 | 论文·交通运输 2025, 25(17): 7365-7372
双半挂汽车列车联合控制下高速横向稳定性
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周庆辉1, 2 , 张博宇1
作者信息
  • 1 北京建筑大学机电与车辆工程学院, 北京 100044
  • 2 北京市建筑安全监测工程技术研究中心, 北京 100044
  • 周庆辉(1973—),男,汉族,山东济南人,博士,教授。研究方向:车辆智能控制技术。E-mail:

High-speed Lateral Stability of Double Semi-trailer Trucks Train with Coordinated Control
Qing-hui ZHOU1, 2 , Bo-yu ZHANG1
Affiliations
  • 1 School of Mechanical-Electrical and Vehicle Engineering, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
  • 2 Beijing Engineering Research Center of Monitoring for Construction Safety, Beijing 100044, China
出版时间: 2025-06-18 doi: 10.12404/j.issn.1671-1815.2409185
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双半挂汽车列车具备较强的运输能力和相对较低的运输成本等优势。然而,与半挂车相比,双半挂汽车列车拥有更多的车辆单元,导致驾驶难度更高且高速横向稳定性更差。针对此问题,提出了模型预测控制(model predictive control,MPC)与差动制动相互结合的控制策略。根据MPC原理,设计了MPC双半挂汽车列车横向稳定性控制器。通过MPC控制3个车辆单元的横摆力矩,结合差动制动技术从而实现各个车轮制动力的分配。在Trucksim搭建了双半挂汽车列车模型且MATLAB/Simulink中建立车辆简化模型。通过Trucksim与MATLAB/Simulink联合仿真,在不同车速、负载、附着系数情况下验证了所设计系统的有效性。研究结果表明:所设计的控制器有效地降低了各车辆单元的质心侧偏角、侧向加速度和横摆角速度等,提高了双半挂汽车列车高速变道时的稳定性。

双半挂汽车列车  /  横向稳定性控制  /  模型预测控制  /  差动制动  /  力矩分配

The double semi-trailer truck train has strong transportation capacity and relatively low transportation costs. However, compared to semi-trailers, double semi-trailer truck train has more vehicle units, resulting in higher driving difficulty and lower lateral stability at high speeds. To address this issue, a control strategy combining model predictive control (MPC) with differential braking was proposed. Based on the principles of MPC, an MPC lateral stability controller for double semi-trailer truck trains has been designed. MPC regulates the yaw moments of three vehicle units, with differential braking technology dynamically allocating braking forces to individual wheels. The double semi-trailer truck train model has been built in Trucksim, and a simplified vehicle model has been established in MATLAB/Simulink. Through joint simulation of Trucksim and MATLAB/Simulink, the effectiveness of the designed system was verified under different vehicle speeds, loads, and friction coefficients. The research results indicate that the designed controller effectively reduces the centroid yaw angle, lateral acceleration, and yaw rate of each vehicle unit, thereby enhancing the stability of double semi-trailer truck train during high-speed lane changes.

double semi-trailer truck train  /  lateral stability control  /  model predictive control  /  differential braking  /  torque distribution
周庆辉, 张博宇. 双半挂汽车列车联合控制下高速横向稳定性. 科学技术与工程, 2025 , 25 (17) : 7365 -7372 . DOI: 10.12404/j.issn.1671-1815.2409185
Qing-hui ZHOU, Bo-yu ZHANG. High-speed Lateral Stability of Double Semi-trailer Trucks Train with Coordinated Control[J]. Science Technology and Engineering, 2025 , 25 (17) : 7365 -7372 . DOI: 10.12404/j.issn.1671-1815.2409185
随着中国经济和物流行业的快速发展,目前所使用的货车已无法满足发展需要,急需引入新型货运车辆。双半挂汽车列车由于具有较好的运输能力、运输成本较低及节约能耗等特点,逐渐成为公路运输领域的主流选择[1]。经过欧洲多国家长期实践,目前提出了3种双半挂汽车列车形式[2],即A-double(半挂车加中置轴挂车)、B-double(牵引车、带有铰接点的挂车加普通挂车)、C-double(牵引车、一挂车、拖台加二挂车)。欧洲主要进行了超长列车性能测试,总长度可达30 m以上[3],但由于国内外对于上路车辆的限制不同,欧洲车辆在国内并不适用。国内为了更好地发展长重型汽车列车,2016年7月,中国工业和信息化部发布《汽车、挂车及汽车列车外廓尺寸、轴荷及质量限值》(GB 1589—2016),该标准取消了车辆长度与最大总质量之间的限制,并放宽了车辆宽度的规定。此外,该标准还引入了中置轴货运挂车及其他新车型[4];2022年前后,中国上汽红岩和吉利远程先后发布了“双挂列车”的产品,为双半挂汽车列车在国内的使用奠定了基础。
双半挂汽车列车由于负载重,尺寸长及车辆单元之间的耦合作用等因素,使其驾驶难度提高,容易发生严重交通事故[5]。因此双半挂汽车列车的高速行驶稳定性问题亟待解决。
目前,针对双半挂汽车列车的结构优化以确保其横向稳定性的研究已经相对成熟[6-9]。研究表明,不同结构的双半挂汽车列车在横向稳定性方面表现各异。Wang等[6]研究验证了通过适当增加后桥车身质量、提升轮胎侧偏刚度、后移质心以及前移铰接点,可以有效增大不足转向梯度,从而改善车辆在行驶过程中的横向稳定性。张义花等[7]总结了结构参数和使用参数以及各种主动控制策略对双半挂汽车列车横向稳定性的影响。基于此,为了进一步提升高速行驶时的横向稳定性,可以采用不同的控制策略。
应用于双半挂汽车列车的控制策略主要包括主动转向和差动制动等[10-12]。与主动转向相比,差动制动具有结构简单、成本更低的优势,且能够更直接有效地改善车辆的横向稳定性[13]
然而,双半挂汽车列车作为一个复杂的非线性动力学系统,受到外部扰动和车辆状态等多种因素的影响[14]。因此,准确获取附加横摆力矩既具有挑战性又至关重要,而不同算法在横摆力矩的获取上效果各异。目前,应用于双半挂汽车列车的控制算法主要包括比例-积分-微分(proportion-integration-differentiation,PID)、线性二次型调节控制(linear quadratic regulation,LQR)和模型预测控制(model predictive control,MPC)等[10-12,15]。与其他算法相比,MPC算法通过优化未来的控制动作[16],能够有效应对多变量约束系统,使得外部扰动等不确定因素能够得到及时调整,从而实现更优的控制效果[17]
基于此,现提出一种结合模型预测控制(MPC)算法与差动制动的双半挂汽车列车横向稳定性控制方法。该方法通过MPC控制器计算得到的横摆力矩,利用差动制动技术对牵引车和挂车的车轮施加制动力,从而有效提升高速行驶时的稳定性。
B-double汽车列车作为双挂汽车的一种,它由牵引车和两辆半挂车组成,各车辆单元通过鞍座进行铰接。如图1所示,参数如表1所示。
B-double汽车列车是多单元系统,内部存在复杂的约束关系[18]。为便于研究,做出以下假设。
(1)车轴的左右两侧车轮组等效为单个车轮。
(2)牵引车与挂车,挂车与挂车间铰接角小。
(3)侧倾和俯仰很小。
(4)将牵引车前轮转角作为动力学输入。
简化图如图2所示。
对牵引车进行受力分析,表达式为
$\left\{\begin{array}{l}m{a}_{y}={F}_{\mathrm{c}\mathrm{f}}+{F}_{\mathrm{c}\mathrm{r}}-{F}_{1}\\ {I}_{z}\stackrel{·}{r}=a{F}_{\mathrm{c}\mathrm{f}}-c{F}_{\mathrm{c}\mathrm{r}}+b{F}_{1}\end{array}\right.$
同理,将第一、二节半挂车分别进行受力分析,表达式为
$\left\{\begin{array}{l}{m}_{1}{a}_{y1}={F}_{1}+{F}_{\mathrm{c}1}-{F}_{2}\\ {I}_{z1}{\stackrel{·}{r}}_{1}={a}_{1}{F}_{1}-{\mathrm{c}}_{1}{F}_{\mathrm{c}1}+{b}_{1}{F}_{2}\end{array}\right.$
$\left\{\begin{array}{l}{m}_{2}{a}_{y2}={F}_{2}+{F}_{\mathrm{c}2}\\ {I}_{z2}{\stackrel{·}{r}}_{2}={a}_{2}{F}_{2}-{b}_{2}{F}_{\mathrm{c}2}\end{array}\right.$
联立方程可得
$\left\{\begin{array}{l}\mathrm{m}{a}_{y}+{m}_{1}{a}_{y1}+{m}_{2}{a}_{y2}={F}_{\mathrm{c}\mathrm{f}}+{F}_{\mathrm{c}\mathrm{r}}+{F}_{\mathrm{c}1}+{F}_{\mathrm{c}2}\mathrm{m}{a}_{y}\mathrm{b}+{I}_{z}\stackrel{·}{r}\\          =(b+a){F}_{\mathrm{c}\mathrm{f}}+(b-c){F}_{\mathrm{c}\mathrm{r}}\\ {m}_{1}{a}_{y1}{b}_{1}+{I}_{z1}{\stackrel{·}{r}}_{1}=({a}_{1}+{b}_{1})({F}_{\mathrm{c}\mathrm{f}}+{F}_{\mathrm{c}\mathrm{r}}-m{a}_{y})+\\         ({b}_{1}-{c}_{1}){F}_{\mathrm{c}1}\\ {m}_{2}{a}_{y2}{a}_{2}-{I}_{z2}{\stackrel{·}{r}}_{2}=({a}_{2}+{c}_{2}){F}_{\mathrm{c}2}\end{array}\right.$
当车辆侧向加速度在0.4g范围内(g为重力加速度),轮胎侧偏角小于5°时,轮胎的侧偏力与侧偏角呈线性关系,即
${F}_{\mathrm{c}i}={k}_{i}{\alpha }_{i}(i=\mathrm{f},\mathrm{r},\mathrm{1,2})$
取状态量$\mathrm{X}=[{v}_{1}\mathrm{r}{\theta }_{1}{\theta }_{2}{\stackrel{·}{\theta }}_{1}{\stackrel{·}{\theta }}_{2}{]}^{\mathrm{T}}$
输入为牵引车前轮转角$\delta $,表达式为
$\mathrm{M}\stackrel{·}{X}=AX+B\delta $
式中:mm1m2分别为各车辆单元的质量;ayay1ay2分别为各车辆单元的侧向加速度;FcfFcrFc1Fc2分别为牵引车前轴、后轴及第一、二节半挂车后轴的侧偏力;F1F2分别为第一、二铰接点处的内力;IzIz1Iz2分别为各车辆单元的转动惯量;rr1r2分别为各车辆单元的横摆角速度;abc分别为牵引车质心到其前、后轴及铰接点的距离;a1b1c1a2b2同理;k为侧偏刚度;α为侧偏角;v1为牵引车横向速度;θ1θ2为第一、二铰接角;${\stackrel{·}{\theta }}_{1}$${\stackrel{·}{\theta }}_{2}$为铰接角速度;M为惯性矩阵;A为状态变量系数矩阵;B为前轮转角系数矩阵。
所提出控制方案示意图如图3所示,它包含四自由度动力学参考模型,上层控制器(MPC控制器),下层控制器(制动力矩分配器)。该参考模型以Trucksim输出的前轮转角作为输入,输出牵引车和挂车横摆角速度等理想值,并将其与Trucksim实际模型输出的横摆角速度等实际值进行偏差计算,作为MPC控制器的输入。MPC控制器的任务是确保双半挂汽车列车各车辆单元的实际状态能够跟随参考状态进行响应,并计算出所需的附加横摆力矩。横摆力矩通过制动力矩分配器实现对不同车轮的制动控制。通过这种闭环反馈控制机制,实现双半挂汽车列车在高速行驶时的横向稳定性控制。
在该系统中,为实现对横摆力矩的控制,引入控制变量$U=[{M}_{1}{M}_{2}{M}_{3}{]}^{\mathrm{T}}$,可得空间状态方程为
$\stackrel{·}{X}={A}_{a}X+{B}_{b}\delta +{B}_{\mathrm{u}}U$
式(7)中:Aa=M-1A,Bb=M-1B;M1M2M3为各车辆单元的横摆力矩;Bu为控制变量系数矩阵。
对式(7)离散化得
$X(k+1)={A}_{\mathrm{a},\mathrm{k}}X\left(k\right)+{B}_{\mathrm{b},\mathrm{k}}\delta \left(k\right)+{B}_{u,\mathrm{k}}U\left(k\right)$
式(8)中:${A}_{\mathrm{a},\mathrm{k}}={A}_{a}T+I$,${B}_{\mathrm{b},\mathrm{k}}={B}_{b}T$,Bu,k=BuT, T为离散步长。
在MPC控制器中,目标函数的设计是为了在每个时间步长上优化控制器的决策,并使实际值与参考值之间的偏差尽可能小。目标函数表达式为
$\begin{array}{l}\mathrm{J}=\stackrel{{N}_{\mathrm{p}}}{\sum _{i=1}}{\left(\frac{ }{ }{Y}_{\mathrm{p}}(\mathrm{k}+\mathrm{i})-\mathrm{Y}(\mathrm{k}+\mathrm{i})\right)}_{\mathrm{Q}}^{2}+\\ \stackrel{{N}_{\mathrm{c}}-1}{\sum _{i=0}}{\left(\frac{ }{ }\mathrm{\Delta }U(k+i)\right)}_{R}^{2}+\rho {\epsilon }^{2}\end{array}$
式(9)中:Np为预测时域;Nc为控制时域;Yp为参考输出;Y为实际输出;$\mathrm{\Delta }U$为控制增量;$Q、R、\rho $分别为状态误差、控制增量和松弛因子的权重系数;$\epsilon $为松弛因子。
对于控制量的约束,通过设置最大限制,确保生成的控制量不会超过车辆物理限制。对于控制增量的约束,目的是限制控制信号的变化幅度,以确保平滑的控制操作和防止过度调整。
$\left|{U}_{\mathrm{k}+\mathrm{i}}\right|\le {U}_{max},i=\mathrm{0,1},2,\dots,{N}_{\mathrm{c}}-1$
$\left|\mathrm{\Delta }{U}_{\mathrm{k}+\mathrm{i}}\right|\le \mathrm{\Delta }{U}_{max},i=\mathrm{0,1},2\dots,{N}_{\mathrm{c}}-1$
$\left|{Y}_{\mathrm{k}+\mathrm{i}}\right|\le {Y}_{max}+\epsilon {I}_{{\mathrm{N}}_{p}\times 1},i=\mathrm{0,1},2,\dots,{N}_{\mathrm{p}}$
式中:Umax为控制量的最大值;$\mathrm{\Delta }{U}_{max}$为控制增量的最大值;Ymax为输出量的最大值。
为简化计算,各车辆单元单侧同轴的轮胎简化为单个轮胎,则各车辆单元的轮胎制动力矩为
${T}_{1\mathrm{l}}={T}_{1\mathrm{r}}=\frac{2{M}_{1}{l}_{1}}{{d}_{1}}$
${T}_{2\mathrm{l}}={T}_{2\mathrm{r}}={T}_{3\mathrm{l}}={T}_{3\mathrm{r}}=\frac{{M}_{1}{l}_{2}}{{d}_{2}}$
${T}_{m\mathrm{l}}={T}_{m\mathrm{r}}=\frac{2{M}_{2}{l}_{3}}{3{d}_{3}},m=\mathrm{4,5},6$
${T}_{n\mathrm{l}}={T}_{n\mathrm{r}}=\frac{2{M}_{3}{l}_{4}}{3{d}_{4}},n=\mathrm{7,8},9$
式中:${T}_{ik}(i=\mathrm{1,2},\dots,9;k=\mathrm{l},\mathrm{r})$分别为牵引车前轴,后轴及一、二节挂车各轴的制动力矩;l1、l2、l3、l4分别为牵引车前轴,后轴及一、二节挂车轴的轮胎半径;d1、d2、d3、d4分别为牵引车前轴,后轴及一、二节挂车的各轴轮距。其制动简化图如图4所示。
对于目标制动车轮的选择,主要由双半挂汽车列车的实际横摆角速度与期望横摆角速度决定。如表2所示。其中,以逆时针方向为正方向。
为了验证上述设计模型的有效性,在Trucksim中建立了非线性车辆模型,与MATLAB/Simulink进行联合仿真。环境因素(如天气等)的变化会影响路面的附着系数,同时随着载重的增加,双半挂汽车列车的横向控制能力会有所下降。因此,设置两种工况,工况一:附着系数μ为0.85、车速v为100 km/h;工况二:附着系数为0.5、车速为88 km/h。并在两种工况下分别对空载、满载两种载重进行单移线工况测试。仿真结果如图5~图10所示,其中最大侧向加速度,最大横摆角速度,最大质心偏转角的绝对值${{a}_{yi,}}_{\mathrm{m}\mathrm{a}\mathrm{x}}、{r}_{i\mathrm{m}\mathrm{a}\mathrm{x}}、{\beta }_{i\mathrm{m}\mathrm{a}\mathrm{x}}(i=\mathrm{1,2},3)$,如表3表4所示。
与无控制情况下相比,所提出的控制方法能够有效降低车辆的最大侧向加速度、最大横摆角速度以及最大质心偏转角的绝对值。由于牵引部分本身较为稳定,因此其控制效果相对较弱。第一节半挂车居中,而第二节半挂车的控制效果则显得尤为明显。与满载状态相比,空载时车辆的稳定性更好,3个车辆单元的最大侧向加速度、最大横摆角速度和最大质心偏转角的绝对值均较低。
图5~图7表3所示,满载时,3个车辆单元的最大侧向加速度降幅分别为8.74%、13.38%和19.86%;最大横摆角速度降幅分别为12.85%、15.23%和15.96%;最大质心偏转角降幅分别为10.07%、14.05%和13.25%。空载时,3个车辆单元的最大侧向加速度降幅分别为13.40%、15.14%和16.79%;最大横摆角速度降幅分别为4.62%、11.04%和12.18%;最大质心偏转角降幅分别为15.41%、16.02%和19.17%。
对于双半挂汽车列车而言,在恶劣天气条件下进行高速变道极具危险性,容易引发瞬态侧翻等事故。这主要是因为湿滑的路面会降低路面的附着系数,从而降低车辆的稳定性。因此,解决恶劣天气条件下的高速变道问题显得尤为重要,以确保车辆的稳定性和人员的安全。工况二的仿真结果表明,所提出的控制方法在改善这一问题方面具有显著效果。
图8~图10表4所示,满载时,3个车辆单元的最大侧向加速度降幅分别为7.78%、13.90%和14.41%;最大横摆角速度降幅分别为12.85%、13.51%和14.14%;最大质心偏转角降幅分别为12.19%、14.77%和13.95%。空载时,3个车辆单元的最大侧向加速度降幅分别为11.15%、12.03%和12.85%;最大横摆角速度降幅分别为3.42%、9.28%和11.59%;最大质心偏转角降幅分别为19.57%、16.11%和18.69%。
针对双半挂汽车列车车辆单元多,负载大,尺寸长等特点,综合考虑MPC与差动制动的控制策略,分析了在参数不确定条件下双半挂汽车列车高速变道过程中的横向稳定性控制问题。在单移线变道工况下,以不同负载、车速和附着系数在MATLAB/Trucksim下联合仿真。有如下结论。
(1)所设计的控制器在不同速度和附着系数的工况下均表现出良好的控制效果,能够有效保持双半挂汽车列车在高速变道时的横向稳定性。
(2)所设计的控制器在变道过程中有效抑制了因双半挂汽车列车重量变化引起的横向失稳,表现出较强的应对车辆重量变化的鲁棒性,可以作为双半挂汽车列车横向稳定性控制的有效方法。
  • 住房和城乡建设部科学技术计划(2022-K-079)
  • 北京建筑大学研究生创新项目(双半挂横向稳定性研究)
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2025年第25卷第17期
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doi: 10.12404/j.issn.1671-1815.2409185
  • 接收时间:2024-12-10
  • 首发时间:2025-12-15
  • 出版时间:2025-06-18
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  • 收稿日期:2024-12-10
基金
住房和城乡建设部科学技术计划(2022-K-079)
北京建筑大学研究生创新项目(双半挂横向稳定性研究)
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    1 北京建筑大学机电与车辆工程学院, 北京 100044
    2 北京市建筑安全监测工程技术研究中心, 北京 100044
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

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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