Article(id=1211010519624970557, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1211010518857412925, articleNumber=null, orderNo=null, doi=10.19620/j.cnki.1000-3703.20230364, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1766657007366, onlineDateStr=2025-12-25, pubDate=1706025600000, pubDateStr=2024-01-24, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1766657007366, onlineIssueDateStr=2025-12-25, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1766657007366, creator=13701087609, updateTime=1766657007366, updator=13701087609, issue=Issue{id=1211010518857412925, tenantId=1146029695717560320, journalId=1189621681917173762, year='2024', volume='', issue='1', pageStart='1', pageEnd='62', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1766657007183, creator=13701087609, updateTime=1766737563605, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1211348397030765064, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1211010518857412925, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1211348397030765065, tenantId=1146029695717560320, journalId=1189621681917173762, issueId=1211010518857412925, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1, endPage=12, ext={EN=ArticleExt(id=1211010519851462975, articleId=1211010519624970557, tenantId=1146029695717560320, journalId=1189621681917173762, language=EN, title=Review on Handling Stability Control of Distributed Drive Vehicles, columnId=null, journalTitle=Automobile Technology, columnName=null, runingTitle=null, highlight=null, articleAbstract=

In order to study the overdrive control of vehicle driven by hub-motor, this paper started from steering control, independent driving control, and joint control of two systems, analyzed the research progress of steering stability control strategy. It firstly classified the aspects affecting the stability based on the steering control technology, then analyzed the characteristics of hierarchical control structure and centralized control structure, as well as the stability research based on different control structures. Finally, combined with the research status and technical progress of the improvement of vehicle operating stability driven by hub motors, the paper predicated future trend.

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为研究分布式驱动车辆的驱动控制问题,从转向控制、独立驱动控制及2个系统联合控制等3个角度入手,分析了分布式驱动车辆操纵稳定性控制策略的研究进展。首先,基于转向控制技术对影响操纵稳定性的方面进行分类;其次,分析了分层式与集中式控制结构的特点;然后,分析了基于控制结构的操纵稳定性研究;最后,结合分布式驱动车辆操稳性改善的研究现状和技术进展,展望了未来的发展趋势。

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王志福(1977—),教授,硕士生导师,研究方向为车辆动力学控制、电机驱动系统控制与测试、多域分布式驱动平台等,
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分布式驱动车辆操纵稳定性控制综述*
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林贤坤 1 , 卢彦希 1 , 王志福 2, 3 , 杨忠义 3 , 叶卓 1
汽车技术 | 2024,(1): 1-12
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汽车技术 | 2024, (1): 1-12
分布式驱动车辆操纵稳定性控制综述*
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林贤坤1, 卢彦希1, 王志福2, 3 , 杨忠义3, 叶卓1
作者信息
  • 1 广西科技大学,机械与汽车工程学院,柳州 545000
  • 2 北京理工大学,机械与车辆学院,北京 100000
  • 3 广西科技大学,自动化学院,柳州 545000

通讯作者:

王志福(1977—),教授,硕士生导师,研究方向为车辆动力学控制、电机驱动系统控制与测试、多域分布式驱动平台等,
Review on Handling Stability Control of Distributed Drive Vehicles
Xiankun Lin1, Yanxi Lu1, Zhifu Wang2, 3 , Zhongyi Yang3, Zhuo Ye1
Affiliations
  • 1 School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545000
  • 2 School of Mechanical and Vehicle Engineering, Beijing Institute of Technology, Beijing 100000
  • 3 School of Automation, Guangxi University of Science and Technology, Liuzhou 545000
出版时间: 2024-01-24 doi: 10.19620/j.cnki.1000-3703.20230364
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为研究分布式驱动车辆的驱动控制问题,从转向控制、独立驱动控制及2个系统联合控制等3个角度入手,分析了分布式驱动车辆操纵稳定性控制策略的研究进展。首先,基于转向控制技术对影响操纵稳定性的方面进行分类;其次,分析了分层式与集中式控制结构的特点;然后,分析了基于控制结构的操纵稳定性研究;最后,结合分布式驱动车辆操稳性改善的研究现状和技术进展,展望了未来的发展趋势。

分布式驱动  /  轮毂电机  /  操纵稳定性  /  独立转向  /  独立驱动

In order to study the overdrive control of vehicle driven by hub-motor, this paper started from steering control, independent driving control, and joint control of two systems, analyzed the research progress of steering stability control strategy. It firstly classified the aspects affecting the stability based on the steering control technology, then analyzed the characteristics of hierarchical control structure and centralized control structure, as well as the stability research based on different control structures. Finally, combined with the research status and technical progress of the improvement of vehicle operating stability driven by hub motors, the paper predicated future trend.

Distributed Drive  /  Hub-motor  /  Handling stability  /  Independent steering  /  Independent driving
林贤坤, 卢彦希, 王志福, 杨忠义, 叶卓. 分布式驱动车辆操纵稳定性控制综述*. 汽车技术, 2024 , (1) : 1 -12 . DOI: 10.19620/j.cnki.1000-3703.20230364
Xiankun Lin, Yanxi Lu, Zhifu Wang, Zhongyi Yang, Zhuo Ye. Review on Handling Stability Control of Distributed Drive Vehicles[J]. Automobile Technology, 2024 , (1) : 1 -12 . DOI: 10.19620/j.cnki.1000-3703.20230364
分布式驱动车辆由多个电机驱动,电机的输出转矩独立、精准可控,可以分别执行驱动和制动指令。同时,分布式驱动汽车结构紧凑、传动效率高,被认为是未来汽车发展的主要方向。由4个轮毂电机驱动的车辆是分布式驱动车辆的一种,具有电机响应速度快等优点。装备有4个独立转向电机的分布式驱动车辆,可以单独对每个车轮的转角进行调整,改善了汽车高速行驶的稳定性,提高了低速行驶的操纵性。
汽车的操纵稳定性是指驾驶员不感到过分紧张、疲劳的前提下,汽车能即时响应驾驶员通过转向系及转向车轮给定的行驶意图,在遭受干扰的情况下汽车能抵御干扰而保持稳定行驶的能力。截止到2022年11月底,公安部统计的数据显示,我国汽车保有量达到3.18亿辆,同比增长1.3%,汽车数量、驾驶员总量均居世界第一。汽车数量的激增导致了道路交通事故安全频发,其中大部分事故是由于汽车在高速行驶时快速转弯、超车变道或转向误操作导致的[1]。因此,对四轮轮毂电机驱动车辆的操纵稳定性进行研究十分必要。
从20世纪60年代开始,国内外学者就陆续研究并提出了几种用于乘用车的转向系统,包括四轮转向(4 Wheel Steering,4WS),主动前轮转向(Active Front Steering,AFS)、主动后轮转向(Active Rear Steering,ARS)及四轮独立转向(4 Wheel Independent Steering,4WIS)[2]。20世纪80年代初,四轮转向系统[3]逐渐成熟,并开始引入乘用车市场,走向商业化。21世纪初,主动前轮转向系统[4]与主动后轮转向系统才逐渐装备于车辆上。依据转向控制系统技术分类,本节的重点主要集中在主动前轮转向、主动后轮转向及四轮转向方面。
主动前轮转向稳定性控制是指在驾驶员输入的基础上,向车轮施加附加转向角,从而提高车辆转向过程中的横摆稳定性,其控制策略如图1所示。
主动前轮转向可以分为机械式与线控式2种,机械式结构主要通过机械连接转向盘至转向传动机构,而线控式结构取消了转向盘与转向轮之间的机械连接,将驾驶员转向指令通过数据总线传递至转向控制系统。机械式主动转向以宝马公司生产的主动前轮转向系统及奥迪汽车的动态转向系统为代表[5-6]。宝马的主动前轮转向包括双行星齿轮机构及电子伺服转向系统,奥迪则在谐波齿轮传动机构的基础上研发了动态转向系统。线控式转向系统采用传感器-处理器-执行器的传输机构,并通过总线进行信号传输[7],以通用公司研发的Sequel概念车为代表。
为了改善前轮转向角跟踪精准度及前轮转向工况下的操纵性与稳定性,文献[8]给出了一种基于线控转向系统的滑模学习控制策略。为了加强滑模控制器对外界环境的适应性与抗干扰性,DO等[9]提出了一种基于自适应快速终端滑模控制方法的前轮转角跟踪控制器,其贡献在于考虑了子系统间的耦合特性,并在此基础上,一定程度地实现了各个子系统的有机结合。黄飞华[10]提出了一种基于多输入、多输出的前轮转向控制系统,其核心是研究了一种以质心侧偏角与横摆角速度间的耦合关系为创新点的滑模控制器,输出结果直观、准确。为了提升车辆在复杂路况下的操纵稳定性,熊璐等[11]基于前馈控制方法,设计了前轮主动转向控制器,提高了横摆角速度稳态增益,进一步通过积分抗饱和滑模控制器改善车辆横摆角速度的瞬态响应。
主动后轮转向是四轮车辆主动转向中的一种转向方式,通过主动控制后轮转角,进而改变轮胎侧偏角,并提供车辆转向时所需的侧向力。需要注意的是,车辆在某些极限状态下,轮胎的侧向力极易达到饱和,此时进行转向控制,容易导致车辆发生侧滑事故。为了改善车辆在极端路况下的操纵稳定性,彭文正等[12]针对装配有主动后轮转向系统的轮毂电机驱动汽车,基于滑模变结构控制方法,设计了主动后轮转向控制器,减小了车辆质心侧偏角。除了滑模控制理论,最优控制理论也被广泛应用。为了改善主动后轮转向车辆在高速工况下转向灵敏度不足的问题,并提高主动后轮转向车辆在低附着路面下的稳定性,付翔等[13]提出了主动后轮转向和四轮转矩分配的协调控制系统,并设计了基于规则的协调控制器,合理分配各子控制器的工作区间。檀振贤等[14]提出了一种基于回正力矩非线性数学模型的主动后轮转向控制策略,并以质心侧偏角为变量,达到改善车辆行驶稳定性的目的,且通过联合仿真对该控制策略进行了验证。
目前,以轮毂电机驱动的四轮转向车辆主要集中在中、高端品牌上[15]。车辆高速行驶时,前、后轮同向转向,使车辆并线运动更灵活;在低速行驶时,前、后轮异向转向,减小了转弯半径,改善了车辆的过弯能力;直线减速时2个后轮可实现前端内收,以增强车辆处于高速工况时的制动操纵稳定性[16]
轮毂电机驱动虽然具有转矩响应精准、独立可控、传动效率高等优点,但存在电子集成化程度高、内部结构复杂等不足。由此,Wang等[17]提出了一种基于 H 控制器的容错线性二次型调节器,并应用于车辆稳定性和操纵性的改善。在容错控制中,同时考虑了失效故障、附加故障等多种故障。为了研究四轮转向汽车所受的侧向力对车辆整体运动控制状态的影响,滑模控制策略广泛应用于车辆稳定性控制领域,Akar等[18]采用了滑模控制算法跟踪线性参考模型输出的质心侧偏角与横摆角速度,且通过仿真验证了滑模控制算法对车辆参数的动态变化具有强鲁棒性。Chen等[19]提出了一种自适应模糊滑模控制(Fuzzy-Slide Mode Control,FSMC)算法,与传统滑模控制相比,可以有效抑制系统的抖振现象,同时保证车辆在复杂路况下的稳定性。
然而,运动中的车辆具有强非线性、强耦合性及参数不确定性[20]。唐秋云[21]提出了一种基于模糊控制算法与传统比例-积分-微分(Proportional-Integral-Derivative,PID)控制理论相结合的自适应模糊比例-积分-微分(Adaptive-Fuzzy PID,AFPID)转向控制器,以改善四轮轮毂电机驱动车辆的转向稳定性,根据PID参数调整经验制定模糊控制规则,增强了系统的自适应性。马士然等[22]设计了PID速度跟随控制器与模糊PID(Fuzzy PID)横摆力矩控制器,以控制各车轮的输出转矩,并通过设置转向角阶跃仿真工况验证了2个控制器在四轮轮毂电机驱动车辆高速转向时的稳定性。此外,马士然等[22]还设计了基于PID与粒子群算法融合的转向稳定性控制器,通过粒子群对全局寻找最优解,可以弥补传统神经网络算法存在的局部最优解缺失、收敛速度慢等不足。同时,设计了基于滑模控制理论的横摆力矩控制器作为对照组,对比验证了粒子群优化算法与PID控制算法融合的转向稳定控制器的高效处理能力。另外,最优控制理论也是一种应用于车辆稳定性控制研究领域的经典控制理论。针对转向和轮胎力分配集成控制出现的耦合问题,分析转向和轮胎力共同影响车辆的横摆响应,王文晓[23]提出利用附加横摆力矩的方式减弱两个控制系统之间的干扰,在预测控制器的基础上,考虑到轮胎特性和垂直载荷的影响,增加摩擦椭圆约束优化轮胎力分配控制。
针对四轮轮毂电机驱动车辆存在的转向不足等现象,孙天民[24]提出了一种双层式电子稳定性控制系统,上层控制器由PID速度控制器、模糊PID质心侧偏角横摆力矩控制器等控制器构成,模糊PID控制器依据模糊控制规则输出横摆力矩,将输出的横摆力矩以驱动转矩命令的形式分配到4个电机驱动控制单元。
本节综述了基于主动前轮转向、主动后轮转向和四轮转向实现操纵稳定性改善的研究进展。其中,分层式控制框架以双层式控制策略为主,具有控制结构简单、逻辑清晰、运算量小、适合多目标控制的优点。集中式控制框架能够进一步综合考虑不同的控制目标,然而控制器设计难度较大,相关研究少于分层式控制框架。
在控制算法方面,以改进的滑模控制与PID理论为主。与传统滑模控制相比,改进后的滑模控制方法改善了传统滑模控制器广泛存在的抖振现象,且对复杂环境的适应性和鲁棒性更强,同时削弱了子系统间的强耦合性。与经典PID控制理论[25]相比,改进后的PID方法融合了神经网络、模糊控制等前沿控制技术,优劣势互补,可以提高控制系统整体的鲁棒性、最优解求解速度及响应速度,以实现改善四轮轮毂电机驱动车辆的操纵稳定性。
虽然4个轮毂电机的独立控制增加了转向控制系统的自由度,为操纵稳定性的改善创造了条件,然而,过多的自由度给车辆的转向控制也带来了困难,尤其是在极限工况下。因此,未来对四轮轮毂电机驱动电动汽车的高速转向稳定性研究需要更多投入。
四轮独立驱动车辆,具有4个轮毂电机单独驱动各个车轮的突出优势,可以实现车轮转矩的解耦控制,以期基于轮毂电机输出的精准驱动/制动力矩实现车辆的稳定性控制,避免传统车辆通过制动以大幅降低车速的方式获得稳定性等情况。四轮独立驱动车辆可以对各车轮施加不同的转矩,以产生附加横摆力矩,以更灵活地操纵车辆。根据这一特征,差动助力转向[26]作为另一种高级辅助转向系统,本质上也是通过汽车左、右驱动或者制动力的不同得以实现。
各车轮由轮毂电机独立控制的四轮车辆属于典型的冗余控制系统[27],为了获得优秀的操纵性能,需要设计合适、即时的控制策略;同时,为了满足四轮独立驱动车辆在极端工况条件下的操纵稳定性,控制策略将更加复杂,基于控制结构的归类,可以分为分层式控制和集中式控制。
目前,关于车辆稳定性与车辆轨迹跟踪控制等稳定性改善的文献多集中在车辆侧向运动方面。其中,涉及到车辆稳定性控制技术的控制策略多基于车辆侧向动力学模型。按照控制架构,控制策略可分为集中式与分层式控制,分层式控制相对于集中式控制具有结构清晰、逻辑明了、运算简洁等优势,且目前的研究主要集中在分层式控制方面,集中式控制架构较少。
车辆的稳定性控制主要是指防止车辆的纵向、横摆及侧倾状态失稳,以维持行驶稳定性。为了改善四轮轮毂电机驱动车辆的稳定性,文献[28]~文献[31]分别采用分层控制思想设计控制器,其中包含参考模型、上层控制器与下层分配控制器,其具体控制框架如图2所示。图中, δ f d为期望前轮转角, β d γ d分别为期望质心侧偏角和横摆角速度,T Δ T分别为经上层控制器计算得到的期望车辆稳定力矩和经下层分配策略后输出给各电机的力矩,Tt为车辆模型实际输出的总力矩,βγuδ分别为车辆模型实际输出的质心侧偏角、横摆角速度、纵向车速及前车轮转角。
为了改善四轮独立驱动车辆的纵向行驶稳定性,主要可以从纵向车速、车辆横摆角速度及车轮纵向力等状态参数入手控制。
王伟达等[28]结合新型的滑模变结构控制理论,提出了一种分层式控制策略,其中上层设计了一种非线性联合滑模变结构控制器,以观测横摆角速度与质心侧偏角的变化并判断汽车是否处于稳定状态,下层基于轮胎稳定裕度阈值进行转矩分配,其贡献在于一定程度上给质心侧偏角与横摆角速度间如何解耦提供了思路。
贺志颖[29]考虑了电机输出转矩误差与复杂路面附着系数对独立驱动车辆直线行驶稳定性的影响,并提出了包括融合滑模控制算法与PID控制理论且适用于跟踪纵向车速的上层控制器,及下层转矩分配控制器在内的分层控制策略。不仅要考虑车辆处于复杂工况时的失稳状态,更要思考单个电机发生故障又未完全失效时,如何保障其行驶稳定性。针对车辆行驶时单个轮毂电机失效的问题,黄健[30]在提出结合滑模控制理论的双层控制策略的基础上,重点设计了应对单个电驱动车轮失效的转矩分配算法,以弥补行驶方向偏差并维持稳定性。李庆望等[31]提出了一种基于规则的驱动力控制双层式分配策略,以改善单轮失效条件下,同轴左、右轮输出转矩不同时车辆表现出的失稳状态。赵霖[32]在此基础上,以左前车轮作为容错控制研究对象,并基于模型预测控制(Model Predictive Control,MPC)理论设计了容错控制器,以结合功能冗余系统实现车辆在单轮故障情况下的稳定性控制。
同时,车轮所受纵向力的影响也是车辆纵向稳定性控制的重要指标,但各车轮的纵向力较难测量,一般通过预估得到。Chen等[33]设计了一种融合高阶滑模控制器与Luenberger观测器的联合观测器,以提高纵向力预测精度,并将观测值引入扩展卡尔曼滤波器中进行二次降噪,以提高整个观测系统的自适应能力。除此之外,纵向力的控制效果也是重要的,张缓缓等[34]提出了一种结合反向传播(Back Propagation,BP)神经网络的PID控制方法,以横摆角速度与质心处侧偏角为控制变量,改善了四轮独立驱动车辆在极限工况下的稳定性。吕斯文[35]提出了一种既能保证车辆行驶稳定,又能对电机的能耗进行优化的控制策略。
车辆运动的稳定性除了体现在纵向控制上,还体现在车辆的多向运动状态上。文献[30]提出了一种兼顾横摆稳定性与协调稳定性综合性能的三层式集成控制策略,其中分别基于滑模控制方法与线性二次型调节器设计了直接横摆力矩控制器(Direct Yaw-moment Controller,DYC)与侧倾稳定控制器,仿真验证了控制器的即时性与可靠性。张利鹏等[36]提出了一种基于反馈最优控制的侧倾稳定性控制器,可以兼顾车辆四轮转矩的协调分配,以及整个车体的侧倾运动控制。文献[32]提出了一组基于博弈理论的对比稳定性双层控制策略,即基于合作博弈与非合作博弈的稳定性控制策略,主要结合了基于博弈理论的上层稳定性控制策略与最优控制附加横摆力矩下层分配策略,以改善四轮独立驱动车辆的侧向稳定性。李家林等[37]提出了一种基于模型参考自适应的直接横摆力矩稳定性控制算法,上层和下层控制器分别采用了李雅普诺夫(Lyapunov)方法和二次规划优化方法,且分别设计了高、低路面附着系数下的不同路况,观察横摆角速度与质心侧偏角响应,结果表明,所设计的控制器控制效果优于传统滑模控制。类似地,严运兵等[38-39]提出了基于结合神经网络与粒子群算法的PID控制策略的直接横摆力矩决策算法,并选择附着系数较低的路面作为研究环境,其贡献在于通过实时更新神经网络权值满足车速较高时轮胎与路面间的受力要求,并有效保证了四轮驱动车辆的操纵稳定性。
以MPC[40-41]理论为典型代表的集中式控制策略,凭借其预测多个状态并同时包含多个控制目标和约束的优势在集中式控制结构中广泛应用[42],其基本结构如图3所示。但集中式控制策略只对固定的工况进行设计,环境适应性一般。图3中,axayv分别为车辆实际输出的纵向、侧向加速度及侧向车速。
Ataei等[43]基于转矩矢量控制方法提出了一种针对电动汽车的集中式集成控制器,该控制器基于MPC,目的是实现车辆的稳定性和防侧翻稳定性的集成控制,仿真结果显示,该集中式集成控制器在不同工况下可以较好地实现车辆的多目标控制,但该控制器算法复杂,仍需大量实车试验才能验证其可靠性。
Li等[44]为车辆纵向和横向稳定性控制找到基于模型的最优解,提出了一种基于非线性模型预测控制的集中稳定性控制器,并以电动轮的滑移角为输入量,使控制系统快速求解各电动轮所需的附加转矩,实时保障车辆的稳定性。Zhai等[45]为了改善轨迹跟踪精度,提出了以质心侧偏角与横摆角速度为自控量的预览模型,并设计了MPC控制器,且在此基础上,设计了滑模控制器以优化轮毂电机的转矩分配,最终达到提升轨迹跟踪精度与保证车辆处于高速行驶时稳定的目的,相较于经典MPC控制器,跟踪精度误差降低了87%左右。
操纵性改善旨在提供接近驾驶员熟悉的线性车辆特性的操控行为,该目标可以通过跟踪由线性自行车模型响应定义的期望响应来实现。通过跟踪与驾驶员的预期命令相对应的期望横摆率响应,可以改善车辆的操纵性。
张金硕[46]在分析车辆影响操纵性因素的基础上,针对低附着系数路面如何改善车辆的操纵性,提出了包含改进滑模控制算法和模糊PID算法的联合上层控制器,将车轮的滑移率变化控制在合理范围内,下层则通过多种力矩分配方式将力矩分配到各车轮,并设计多种工况对比验证策略的可行性。王成[47]提出了一种双层式直接横摆力矩控制策略以改善四轮轮毂电机驱动车辆的操纵性,决策层设计了基于前馈和反馈方法的联合控制器,执行层采用了附加横摆力矩分配器,提高了负责汽车操纵性的子控制器的响应速度与抗外界干扰能力。Liu等[48]提出了结合模糊滑模控制器和横摆力矩最优分配控制器的双层控制策略以确定4个车轮所需的最佳纵向力。Pan等[49]提出了一种基于非线性模型预测的分层式运动控制器,计算效率提高了近70%,控制误差下降了30%~40%。
在以改善操纵性为目标的集中式控制策略中,MPC等理论被广泛运用,所设计的控制器旨在跟踪所需的横摆角速度,以改善操纵性能。陈琦等[50]针对四轮独立驱动(4 Wheel Independent Drive,4WID)车辆所具有的独特车身结构,结合MPC理论改善了车辆在极限工况下的操纵性。
Russell等[51]基于4WID车辆平台,提出了一种基于轮胎力跟踪的方法来实时修正车辆操纵能力的策略。文献[43]提出了一种基于模型预测控制的集成控制器,以调整施加到车轮上的扭矩及改进4WID的操纵性,其优势体现在适应多种极限工况。在此基础上,Liang等[52]提出一种新型的自适应多模型预测控制策略,其优势在于引入权重自适应机制,提高控制器对各种驾驶工况的适应性,尤其是一些极端工况的操纵能力。秦钢[53]针对轮毂电机的响应速度,研究了谐波抑制算法,从电机响应速度与横摆角速度两个方面改善了四轮独立驱动车辆的操纵性。在汽车操纵稳定性控制中,存在许多非线性状态及过程,多模型预测控制为解决非线性控制问题提供了思路,可以凭借其滚动优化的优势,对车辆模型出现的干扰、失真、畸形等问题进行实时修正,以保障车辆具有良好的操纵性。
本节综述了基于分层式、集中式的车辆操纵稳定性改善控制策略,分析了滑模控制方法与PID控制方法在操纵稳定性改善中的作用。控制架构以分层式控制框架为主,集中式控制框架较少。分层式控制框架中,上层控制以PID理论、最优控制理论等理论为主,下层控制通过转矩分配控制策略进行转矩分配。集中式控制框架以改进后的模型预测控制为主,以其能同时预测多种运动状态、处理不同约束为优势。
四轮独立驱动车辆在行驶过程中的操纵稳定性控制策略研究是非常重要的,以分层式控制框架为代表的控制是未来研究的重点。但分层式控制框架也有其局限性,汽车工程应用中,会存在如各子系统冲突等不足。
四轮独立驱动-独立转向(4WID-4WIS)车辆拥有4个驱动电机,具有四轮独立驱动功能,可以更容易地实现主动前轮转向和直接横摆力矩控制功能,并具有独特的主动后轮转向功能,有利于提高操纵稳定性。并且,4WID-4WIS车辆具有四轮独立转向功能,可以极大减小转弯半径,甚至实现原地转弯,如比亚迪仰望系列。这一特性意味着4WID-4WIS车辆更灵活,具有更好的机动性,有利于实现高精度轨迹跟踪,同时保持更好的操纵稳定性。除轨迹跟踪外,如何改善稳定性控制系统的鲁棒性也是亟需关注的问题。与此同时,车辆多个子系统之间的集成分配控制效果也是影响4WID-4WIS车辆操稳性的重要因素。
分层式控制在满足整体性能的前提下,每一层有相对独立的控制目标,降低了控制系统的复杂程度。Hang等[54-56]以4WID-4WIS车辆为平台,设计了联合轨迹跟踪控制器,并通过与前轮主动转向控制车辆平台、前轮主动转向与直接横摆力矩控制车辆平台和四轮独立转向控制车辆平台作了对比,以验证其优越性。
对于车辆稳定性控制中存在的多执行器控制分配问题,学者们开展了深入的研究。其主要特征是将横摆力矩分配公式转化为一个优化问题,并采用了包括若干种算法的分层式控制策略来优化车辆的稳定性。其中:上层控制器根据车辆运动意图产生参考控制量,如期望横摆角速度,采用反馈控制计算车辆所需的广义力/力矩;下层控制器采用控制分配方法计算分配到各电动轮上的轮胎纵向力,以满足上层控制器输出的总控制量要求,广泛采用的控制方法有加权伪逆控制分配[57-59]、定点控制分配方法[60]、基于加权最小二乘法的非线性优化[61]和模型预测控制[62]等,均采用了双层式控制策略。分层式控制框架又可以细分为耦合控制框架[63]和解耦控制框架,在耦合控制框架中,纵向运动控制与横向运动控制相耦合,从而需要进行多目标控制,控制系统复杂。相较而言,基于解耦控制框架设计的控制器结构简单,易于实现。
4WID-4WIS电动汽车各个车轮的制动和驱动扭矩可以独立控制,较容易实现直接横摆力矩控制并可通过外部横摆力矩弥补轮胎侧向力的不足,从而提高汽车的稳定性。Huang等[64]提出了一种基于反向传播神经网络-比例-积分-微分控制器(Back Propagation-Proportion-Integral-Derivative Controller,BP-PID)的多模型控制系统,用于通过直接横摆力矩控制提高横向稳定性。Liang等[65]设计了一种基于修正线性二次调节器(Linear Quadratic Regulator,LQR)的直接横摆力矩控制方法,以实现车辆的动态稳定性控制。Zhao等[66]提出了一种基于滑模控制的直接横摆力矩分层控制策略,以提高极限行驶条件下的横向稳定性。Chen等[67]通过用相平面法计算稳定性边界,设计了一种新的可拓协调控制器,以提高驾驶稳定性,实现AFS和DYC之间的最佳平衡。为了提高横向稳定性,将一种具有改进结构的鲁棒内模控制方法应用于AFS+DYC[68]的集成控制器设计。
由于鲁棒控制在处理参数不确定性、外部扰动和传感器噪声方面具有优越性,学者对其进行了大量研究,并将其应用于4WID-4WIS车辆的稳定性控制,包括 H 2控制、 H 控制和μ-综合控制[69]。Zhao等[70]设计了一个用于稳定性控制的H2/H混合鲁棒控制器。Li等[71]将具有H性能的预补偿解耦控制应用于纵向运动控制和稳定性控制。Yin等[72]在车辆模型中考虑了变化的参数,并为四轮转向车辆设计了μ-综合控制器。尽管鲁棒控制方法在处理参数扰动时表现出很强的鲁棒性,但缺点也很明显,即高阶控制器运算量大。在控制器设计中,需要在控制性能和计算效率之间找到平衡。
集中式控制结构根据全局动力学优化目标,结合车辆的实时状态信息,通过集中控制器统一控制各执行器。相比于分层式控制结构,集中式控制结构具有较好的全局控制效果,能更大程度地发挥各执行器的性能。但这种控制方式因算法集成度高、参数设定困难,在一定程度上削弱了系统的拓展性和控制算法的鲁棒性[73]
Wang等[74]还考虑了系统延迟和所接收数据不完整等干扰因素,通过H鲁棒控制成功实现了高精度轨迹跟踪,且改善了系统的稳定性。Mashadi等[75]研究了在较强干扰下的轨迹跟踪问题,并结合Simulink所建立的单轨模型,采用μ-综合方法,获得了良好的控制效果。为了克服各种干扰因素,Cui等[76]将MPC引入到轨迹跟踪中,使用多约束MPC方法来实现高速工况下不同附着系数道路的轨迹跟踪控制,并改善了车辆在高速行驶时的轨迹跟踪稳定性。Li等[77]在研究轨迹跟踪一体化协调控制策略时,结合了MPC理论,并通过不同路面附着系数下的仿真试验,分别实现了良好的跟踪性能,且提升了车辆在行驶时的稳定性。
近年来,在车辆的主动安全性、稳定性越来越受到重视的同时,学界对车辆操纵性的研究兴趣也愈发浓厚。操纵性的控制目标是将车辆的横摆角速度控制在理想阈值以内。
基于之前对分层式控制框架的研究,对车辆的控制效果主要还是体现在横摆力矩的分配上,通过产生作用在车辆的附加横摆力矩主动干预车辆的行驶状态,改善车辆的操纵性。
Lu等[78]提出了一种基于电子稳定控制及四轮主动转向控制的双层式控制策略,其中:上层基于二自由度车辆模型,设计了滑模控制器完成虚拟控制量的计算;下层考虑边界约束,采用加权广义逆控制分配方法分配施加到各车轮上的力矩。Goggia等[79]考虑了由于车辆总线信号传输引起的延迟问题,提出了基于积分滑模控制方法以实时跟踪车辆横摆速度的控制策略,仿真验证了基于此策略的车辆操纵性改善的性能。Wang等[80]针对车辆动态控制问题,包括对车轮滚动阻力、车辆实时状态等不确定性因素进行考虑,设计了非线性滑模控制器以实现对实际滑移率与侧偏角的跟踪,达到对各车轮驱动/制动及转向的独立操纵。余卓平等[81]建立了7自由度车辆模型,并采用了双层式控制策略,其中,上层采用前馈与反馈控制方法进行车辆横摆角速度与侧偏角的跟踪,下层基于轮胎摩擦椭圆约束对上层规划的横摆力矩进行分配,使得附着利用率降低,车辆的操纵性明显提高。Jin等[82]针对实时变化的纵向速度对横向稳定性的影响,建立了线性变参数的多面体车辆模型,又考虑到车身轴向载荷分配与轮胎转向刚度的不确定性,设计了鲁棒增益调度控制方法实现对期望横摆率与车辆侧偏角控制,改善了操纵性。李少坤[83]研究了双层式控制策略,其中,上层设计了滑模控制器,以实现对横摆角速度和质心侧偏角的联合控制,下层基于对轮毂电机转矩特性及4WID-4WIS车辆动力学特性的考虑,对各轮毂电机的输出转矩进行分配,通过控制滑移率来保持车辆的操纵稳定性。
除了以上所述的分层式控制策略,关于集中式4WID-4WIS车辆操控性策略的研究仍较少。这主要是因为集中式控制算法集成度高,参数整定困难,在一定程度上削弱了系统的拓展性。
为了改善车辆在相应车道持续行驶过程中的操纵稳定性,Wang等[84]在势场函数中引入横摆角调整因子来降低横摆角速度,进而设计基于车辆安全边界的转向/制动系统协调控制,以提高车辆的横向稳定性。Fan等[85]提出了一种基于滑模变结构控制的AFS控制器,并设计了结合主动前轮转向与电子稳定程序的协调控制器,以提高稳定性。
集中式控制框架结构的相关研究较少,其主要原因为:
a. 集中式控制策略的设计需要引入更多的状态变量,导致控制系统中状态方程的阶数较高,运算量巨大,物理实现成本昂贵。目前,集中式控制大量运用了MPC理论,如Brown等[86]针对轮胎纵侧向力耦合特性,基于非线性模型预测控制理论设计了轨迹规划与控制综合控制器,并通过实车测试,证明了控制器在紧急避障过程中协调纵、侧向力的能力,但出现的非凸优化问题会导致无法寻找全局最优解。
b. 集中式控制框架可以考虑更多的控制目标,但各个控制目标之间存在强耦合性。以纵向-侧向-垂向运动控制为例,在MPC控制框架下可以将纵-侧向控制转化为同一带约束的优化问题,以进行解耦控制。与分层式控制表现出的模型简单、计算量少等优势相比,集中式控制器设计可以免于轨迹规划不可行等情况的出现。
c. 集中式控制框架有更多的参数需要协调,控制器参数不易整定。
本文分别从转向控制、独立驱动控制、转向与独立驱动联合控制这3种控制方案,稳定性、操纵性和操纵稳定性这3种控制目标,分层式与集中式2种控制结构的角度,综述了轮毂电机驱动汽车操纵稳定性控制策略。其中,围绕轨迹跟踪、轮胎力估计、转矩分配、如何在极限工况下(路面附着系数低、车速高)提高系统的鲁棒控制等具体问题,比较了在2种控制框架下,通过必要的参数指标,如横摆角速度、质心侧偏角、轮胎滑移率,评判操纵性与稳定性是否有所改善。
结合当前该领域现状,从转向控制、独立驱动控制及两者联合控制方面对未来轮毂电机驱动车辆操纵稳定性的发展进行了展望。优秀的操纵稳定性是轮毂电机驱动车辆研究的主要目标。影响操纵稳定性的因素有很多,因此,为了贴合实际的驾驶需要,开发实时性高、逻辑简明、鲁棒性强、环境适应性优异的控制策略将成为未来操纵稳定性控制发展的主要方向。但控制策略在实际工程应用中存在运算速度、执行部件响应速度、系统耦合影响等方面的问题,如:MPC控制器计算负担大,车载控制器所提供的算力有限,容易造成信号延迟,引发安全事故;MPC控制策略的实现基于精度较高的车辆模型,因此需要大量的车辆状态参数的输入,会导致车载CAN总线负载率过大,信号延迟率高。上述问题需要在后续的试验与理论研究中加以改进,以实现控制效果的优化。
  • *混合动力重载越野车辆分布式电驱动底盘重构与能效优化控制(52072037)
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2024年第卷第1期
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doi: 10.19620/j.cnki.1000-3703.20230364
  • 首发时间:2025-12-25
  • 出版时间:2024-01-24
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*混合动力重载越野车辆分布式电驱动底盘重构与能效优化控制(52072037)
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    1 广西科技大学,机械与汽车工程学院,柳州 545000
    2 北京理工大学,机械与车辆学院,北京 100000
    3 广西科技大学,自动化学院,柳州 545000

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王志福(1977—),教授,硕士生导师,研究方向为车辆动力学控制、电机驱动系统控制与测试、多域分布式驱动平台等,
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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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