Active Suspension Control; Performance Comparisons Using Control Laws Applied to a Full Vehicle Model

Based on a mathematical model of an actively suspended vehicle, the effects of the following issues in deriving the control laws are studied:

(a)representation of the ground surface as integrated or filtered white noise.

(b)cross-correlation between left and right track inputs.

(c)wheelbase time delay between front and rear inputs.

The third of these issues is shown to be by far the most important. Considerable improvements at the rear suspension can be obtained if the control law includes the information that the rear input is simply a delayed version of the front input. Effectively this provides feedforward terms in the control law for the rear actuator. For the full state feedback case, these improvements are indicated by reductions in the rear body acceleration and rear dynamic tyre load of around 20% and 40% respectively with no increase in suspension working space.     

汽车磁流变非线性悬架模糊控制

建立了整车悬架系统的三维模型,根据试验数据得出了前后悬架弹簧的非线性特性曲线。前后悬架减振器均采用磁流变减振器,采用Bouc-Wen参数化模型为其阻尼力模型。采用模糊控制算法为整车半主动控制算法,采用ADAMS和Matlab联合对整车平顺性进行仿真。结果表明,采用模糊控制算法控制磁流变非线性悬架可提高整车的平顺性。     

基于AI选型的汽车悬架系统参数化设计流程构造

建立了用于汽车悬架系统选型的多层前馈型人工神经网络模型,它采用自适应调整的BP算法。选型推理的依据有：轴距、前轮距、后轮距、整备质量、驱动形式、最高车速、制动形式、发动机最大功率和发动机排量。推理结果为前、后悬架的具体型式。经测试,其与实际情况的吻合率,对前悬架达到了100％,对后悬架达到了70％。进而开发出典型悬架型式的三维参数化设计模板,并集成相关技术环节,构造出基于AI选型的悬架系统参数化设计的一般流程。     

基于多目标粒子群优化算法的某轻型商用车操纵稳定性优化研究

针对某轻型商用车稳态回转时侧倾度偏大的问题对其悬架进行优化改进。基于ADAMS/car搭建整车多体动力学模型,通过前悬架反向平行轮跳试验、后悬架理论计算验证了悬架仿真模型的准确性。进行整车稳态回转工况和转向盘中间位置转向工况仿真分析,结果表明,车身侧倾度偏高。为实现操纵稳定性优化分析的流程自动化,提出了基于modeFRONTIER的联合仿真方法。以悬架设计参数为优化变量,以汽车的侧倾度与横摆角速度响应滞后时间为优化目标,采用拉丁超立方试验设计方法拟合得到混合代理模型,并结合多目标粒子群优化算法对悬架系统进行多目标优化,获得了悬架系统优化方案。优化结果显示,在不影响平顺性的前提下,汽车车身侧倾度降低了13.93%,横摆角速度响应滞后时间降低了2.75%,整车操纵稳定性得到了提升。     

6自由度半车悬架解耦及其分层振动控制的研究

通过对6自由度半车悬架簧载质量的受力分析,推导出其前后1/4悬架间的定量耦合关系,并以其为基础构建分层振动控制算法.中央控制层以悬架质心处的垂向加速度和俯仰角加速度为控制目标,前后两个1/4悬架构成的两个底层分别采用H_∞和LQR控制策略,并接受中央控制层的协调指令.利用MATLAB的仿真表明,与传统控制相比,分层控制由于前后两个1/4悬架的控制量可以并行解算,计算时间大幅缩短,因而可针对路面激励实施详尽的控制,达到了改善车辆行驶平顺性的目的.     

汽车非线性半主动悬架的模糊神经网络控制

考虑磁流变减振器阻尼力和悬架弹性元件非线性特性，建立车辆6自由度的半主动悬架非线性动力学模型。提出了一种基于模糊神经网络系统结构的模型参考自适应控制方法来研究汽车半主动悬架的非线性控制问题，并考虑半车模型前后悬架的输入时滞，对其进行了仿真研究。研究结果表明：运用模糊神经网络非线性控制方法能够使人体和车身垂直加速度、俯仰角加速度都得到很大的衰减，证实这种模糊神经网络控制方法可大大减少路面对车身的振动冲击，提高汽车行驶平顺性。     

Analysis of vehicle rollover dynamics using a high-fidelity model

Recent data show that 35% of fatal crashes in sport utility vehicles included vehicle rollover. At the same time, experimental testing to improve safety is expensive and dangerous. Therefore, multi-body simulation is used in this research to improve the understanding of rollover dynamics. The majority of previous work uses low-fidelity models. Here, a complex and highly nonlinear multi-body model with 165 degrees of freedom is correlated to vehicle kinematic and compliance (K&C) measurements. The Magic Formula tyre model is employed. Design of experiment methodology is used to identify tyre properties affecting vehicle rollover. A novel, statistical approach is used to link suspension K&C characteristics with rollover propensity. Research so far reveals that the tyre properties that have the greatest influence on vehicle rollover are friction coefficient, friction variation with load, camber stiffness and tyre vertical stiffness. Key K&C characteristics affecting rollover propensity are front and rear suspension rate, front roll stiffness, front camber gain, front and rear camber compliance and rear jacking force.     

Optimal Preview Control of Rear Suspension Using Nonlinear Neural Networks

The performance of neural networks to be used for identification and optimal control of nonlinear vehicle suspensions is analyzed. It is shown that neuro-vehicle models can be efficiently trained to identify the dynamical characteristics of actual vehicle suspensions. After trained, this neuro-vehicle is used to train both front and rear suspension neuro-controllers under a nonlinear rear preview control scheme. To do that, a neuro-observer is trained to identify the inverse dynamics of the front suspension so that front road disturbances can be identified and used to improve the response of the rear suspension. The performance of the vehicle with neuro-control and with LQ control are compared.     

Optimal Control of a Vehicle Suspension Incorporating the Time Delay between Front and Rear Wheel Inputs

An optimal control law for a vehicle suspension is developed using a discrete linear quadratic regulator framework. The time delay between the disturbance due to the road at the front and rear wheels is incorporated into the model, and it is shown that the optimal control law requires information gathered at the front wheels. A comparison is made between the optimal control law and a suboptimal one which does not incorporate front wheel road information.     

A Pitch-Plane Model of a Vehicle with Controlled Suspension

A vehicle model incorporating front and rear wheel suspensions and seat suspension is presented. The suspension control includes algorithms to provide both dynamic and steady state (levelling) control. Vehicle response to (a) vertical inputs due to ground disturbances at the wheels and (b) longitudinal inputs due to the inertial forces during braking and accelerating, are investigated. It is shown that the static (self-levelling) control causes a slight deterioration in dynamic performance. The active ride control produces improvements of ride comfort under dynamic conditions compared to an equivalent passively suspended vehicle. In steady state the proposed control eliminates the error heave of the body caused by tilting of the vehicle with active suspension.     

一种典型的多连杆后悬架ADMAS仿真分析

利用ADMAS对一种典型的多连杆后悬架进行多体动力学建模和仿真分析,对该悬架的基本特性作出评价并通过K＆C试验台验证仿真模型的正确性,从而获得一套典型多连杆后悬架的虚拟样机。     

基于多刚体动力学理论的摩托车平顺性仿真研究

基于ADAMS软件,对摩托车后悬摆臂长度和后悬弹簧倾角变化时,后悬架的非线性特性进行分析,并建立了摩托车整车系统参数化振动模型,以B级路面谱为振动激励对某骑式摩托车进行随机振动分析,综合研究了摩托车悬架弹簧刚度和阻尼、后悬摆臂长度以及摆臂与弹簧间夹角对整车平顺性的影响。     

Optimal Preview Control of Rear Suspension Using Nonlinear Neural Networks

SUMMARY

The performance of neural networks to be used for identification and optimal control of nonlinear vehicle suspensions is analyzed. It is shown that neuro-vehicle models can be efficiently trained to identify the dynamical characteristics of actual vehicle suspensions. After trained, this neuro-vehicle is used to train both front and rear suspension neuro-controllers under a nonlinear rear preview control scheme. To do that, a neuro-observer is trained to identify the inverse dynamics of the front suspension so that front road disturbances can be identified and used to improve the response of the rear suspension. The performance of the vehicle with neuro-control and with LQ control are compared.     

Mazda 6轿车底盘结构设计分析

对Mazda6轿车底盘系统结构特点进行了介绍。其前悬架系统采用A型高支点双横臂独立悬架，不同于传统机构之处在于其下控制臂为双铰点结构；后悬架采用独特的E型多连杆结构；后副车架在不增加质量的前提下提高了刚度与强度；采用的侵入最小的制动踏板机构系统能有效地减缓对驾驶员身体下部的冲击力；所装备的与行驶稳定性有关的控制系统能有效地提高整车性能。     

Hierarchical modeling of semi-active control of a full motorcycle suspension with six degrees of freedoms

Hierarchical control is a new control framework in the vehicle vibration control field. In this paper, a hierarchical modeling method is presented to form a different motorcycle model, compared to the traditional model with six degrees of freedoms (DOF), so as to construct hierarchical modeling control. The whole control framework is composed of a central control, two local controls and two uncontrollable parts. The front and rear wheel systems of a motorcycle are all dealt with by using two independent local 2-DOF systems. The driver and engine act as uncontrollable passive parts. The central control is composed of an algorithm made up of some dynamic equations that harmonize local relations. The vertical and pitch accelerations of the suspension center are treated as central control objects. With the help of Linear Quadratic Gaussian (LQG) algorithms adopted by two local controls, respectively, and Matlab software, some results of the simulation show that hierarchical modeling control requires less CPU time, reduces respond time and improves ride quality.     

悬架-轮胎-胎面自激振动系统建模与分析

为了研究轮胎不均匀磨损与悬架特性的关系,建立了悬架-轮胎-胎面自激振动系统理论模型。在对扭杆梁式后悬架进行力学分析等效的基础上,综合了轮胎-胎面摩擦系统,在ADAMS中建立了能够反映车轮定位参数（前束角、外倾角）和胎面侧向位移的系统模型。通过仿真分析得到了自激振动系统的振动特性,有利于进一步的整车动力学分析和轮胎不均匀磨损研究。     

轿车扭杆式半独立后悬架动力学仿真和试验研究

结合轿车扭杆式后桥的等效刚度力学模型、后悬架非对称橡胶衬套的变形耦合特性以及减振器、螺旋弹簧的力学模型,建立轿车扭杆式半独立后悬架的等效动力学模型。对模型进行仿真分析和试验研究,通过对比表明仿真和试验的结果具有较高的一致性。     

基于神经网络的轻型客车悬架参数的优化

应用四轮相关的时域路面随机输入模型的不平度时间序列，对轻型客车的八自由度非线性平顺性模型进行了分析研究，并将计算出的平顺性指标作为输入，以径向基函数神经网络对后悬架参数进行了优化计算。算法准确，合理，为平顺性的进一步研究提供了新的方法。     

扭转梁后悬架C特性对转向特性的影响分析

为分析扭转梁C特性对稳态转向性能的影响,文章以模态综合法建立了某乘用车扭转梁后悬柔性模型,对建立的原型车进行侧向力C特性仿真,与对标车进行对比发现仿真与试验值存大较大差异,且侧向力前束特性存在较大的过度转向趋势.通过优化安装衬套的刚度使得原型车与试验结果吻合,最后对整车进行稳态回转仿真发现,负的前束侧向力特性不利于转向,优化后的模型提高了整车不足转向.     

一种新的四连杆悬架刚柔耦合模型

在动力学仿真软件Adams/View模块中建立了四连杆后悬架模型,该模型考虑了轮胎的特性对悬架定位参数的影响.分析了四连杆后悬架的结构及杆件刚度对悬架定位参数的影响,并求出纵拖臂的自由模态,生成纵拖臂柔性体模型后替换原来的刚性杆件,建立悬架的刚柔耦合模型.