多输入多输出试验系统的随机波形时域再现

提出用多输入多输出CARMA模型描述多输入多输出试验系统，解决了单输入单输出CARMA模型应用于汽车道路模拟试验台试验系统时存在的局限性。用变遗忘因子的增广最小二乘法递推算法识别参数，结合隐式最小方差自校正调节器，在电液伺服道路模拟试验台上实现多点随机波形再现。     

用CARMA模型再现随机波形

利用被控系统的输入量、输出量和期望输出值所构成的时间序列建立CARMA模型。通过对该模型参数进行实时估计，计算出自校正控制器控制量。本文通过分析，建立CARMA模型，在电液伺服汽车道路模拟试验台上实现了位移和加速度随机波形的波形再现。     

整车室内道路模拟试验系统特性识别的研究

为进行有效的整车室内道路模拟试验,首先要通过系统特性识别获得整个试验系统的频率响应函数矩阵,而后在目标信号模拟过程中获得满足要求的驱动信号。本文详细叙述了系统特性识别原理、过程以及对识别的模型进行评判的方法,并结合具体的试验进行了验证。     

基于迭代学习控制的道路模拟算法

研究了迭代学习控制在道路模拟试验台上的应用。将道路模拟控制系统所要实现的控制任务归结为有限时间区间上的完全跟踪问题,在不辨识系统模型的情况下,根据对象系统的先验知识,直接设计迭代学习律。使用了一种平滑滤波器型开环迭代学习控制,以提高系统的鲁棒性和控制品质。试验结果表明所设计的控制系统令人满意。     

道路模拟试验中系统非线性特性的研究

通过对响应信号的幅频特性和系统的频响函数分析,详细讨论了非线性系统的辨识和系统的解耦.最后建立典型零部件(副车架)道路模拟的试验台架,计算试验台架的频响函数,利用RPC迭代得到准确的驱动谱,并进行试验验证.试验结果表明,试验模拟精度可以满足要求.     

基于道路模拟试验的并联机器人MIMO自适应控制算法研究

道路模拟试验系统成为现代汽车检测的主流手段,文章设计了一种基于道路模拟试验的并联机器人MIMO自适应控制算法,对其过程识别模块、系统识别模块以及控制的参数设计模块进行了简单的介绍,并通过Matlab仿真来验证算法的优势所在。     

基于小波和粒子群算法的HEV行驶状况辨识方法研究

针对混合动力汽车(HEV)行驶状况(道路坡度和整车载荷)变化难以有效识别,导致驱动系统控制策略不能有效满足驾驶员意图问题,以混联式HEV为研究对象,提出了基于小波滤波和粒子群算法的HEV行驶状况辨识方法。首先建立了汽车行驶状况辨识模型,采用最小二乘法确立了优化目标函数,其次研究了基于小波滤波和粒子群算法的HEV行驶状况辨识原理,最后进行了行驶状况粒子群智能算法辨识试验。在采集实车数据的基础上,对实车数据进行小波滤波,并运用行驶状况辨识方法对道路坡度和整车载荷进行了辨识,并对辨识结果进行小波滤波,结果表明,试验工况下整车载荷辨识的相对误差绝对平均值为2.71%,道路坡度辨识的相对误差绝对平均值为3.85%,验证了所提出方法的有效性。     

三轴客车室内道路模拟试验研究

对采集的道路载荷谱进行分析处理得到道路模拟试验的目标信号。确定了道路模拟试验系统的输入和输出的通道配置。采用白粉噪声进行系统识别。进行了道路模拟中的第一次驱动信号计算以及循环迭代,最终达到预定的模拟精度,为整车室内耐久试验奠定基础。结果表明,基于道路模拟技术,在室内复现三轴客车的路面激励工况,该方法可行、有效。     

基于系统参数估计的重型汽车燃油经济性分析

在传统汽车动力性模型基础上,采用系统参数辨识技术,利用汽车道路滑行和加速试验得到的相关数据,识别出动力性模型中的主要参数:空气阻力系数、滚动阻力系数和传动系机械效率,有效解决了在缺少有关参数时对汽车燃油经济性的分析问题,并取得满意的结果。     

应用道路模拟机进行整车强化坏路试验研究

应用道路模拟技术将道路试验转移到台架试验,通过兼顾时域、频域、伪损伤的方法确保迭代精度,大大缩短道路试验周期。详细分析路谱采集和路谱编辑处理的方法,探讨试验台架系统识别中如何确保频响函数精度、路谱迭代精度的关键技术。以石块路为例,经过多次的迭代,根据时域、频域和伪损伤分别对迭代质量进行评价,发现模拟响应信号和道路原始响应信号吻合,实现载荷谱再现。最后通过台架耐久性试验,更快地再现了强化坏路试验中的车辆故障。     

基于汽车车身垂直加速度的典型道路路面谱识别研究

以1/4汽车振动模型为研究对象,推导出以汽车车身垂直振动加速度作为输入信号、路面不平度作为输出信号的数学模型及其模拟图,并利用MATLAB/SIMULINK搭建系统模型求解路面不平度,对路面不平度进行谱估计完成路谱的识别。通过实际测试和数据处理分析,说明该方法理论依据正确可行,可以为虚拟样机仿真路面的生成提供数据支持。     

基于路面自动识别的ABS自适应神经模糊控制器仿真研究

有效、快速的道路状况自动识别对于提高ABS性能具有重要意义。通过仿真试验分析,提出了一种比传统方法更快更高效的路面识别方法,并设计了以滑移率为控制目标的ABS模糊神经网络控制器。结合车辆模型熏对单一附着系数路面和变附着系数路面进行了ABS制动模拟试验。结果表明熏基于路面自动识别ABS模糊控制系统能快速、准确判断出路面状况的变化熏自动调整、优化控制器控制参数熏使车辆获得最大地面制动力,与传统利用车身加速度进行路面识别的逻辑门限控制器相比,该控制器反应更灵敏,控制更精确。     

Nonlinearity-induced time-varying harmonic dynamic axle load and its impact on dynamic stability of car–trailer combinations

A nonlinearity-induced time-varying harmonic dynamic axle load is found in the road test of a car–trailer combination. To understand its influence on system dynamic stability, a corresponding linear single-track model (STM) is proposed. System dynamic stability is described and sensitivity analysis for the system parameters is achieved. The contribution of the harmonic force is quantified by a derived effective axle load. Because the harmonic effect might be time varying in practice, a time–frequency analysis-based parameter identification method is introduced. Experimental study shows that a time-varying harmonic effect really exists. A yaw-rate-based simulation method is designed to simulate this behaviour. The sensitivity analysis of the influence of the harmonic amplitude or phase on dynamic stability is performed with a simulation study. With appropriate modification of the harmonic amplitude and phase shift applied in selected time windows, the time-varying system characteristics in the road test can be simulated very well.     

Experimental modeling of controlled suspension vehicles from onboard sensors

In this article, identification of vertical dynamics of vehicles with controlled suspensions is considered. Identification is performed from experimental data measured on a four-poster bench test of a segment C car, equipped with a CDC-Skyhook dampers control system. The measurements are obtained from the onboard accelerometers needed by the control system. A nonlinear model in regression form is identified, having the road profile and damper control currents as inputs and chassis accelerations as outputs. The model is identified by means of a set membership structured identification method, which takes advantage of physical information on the structure of the system, decomposing the system into three subsystems: one represents the chassis and engine and the other two represent the overall behavior of front and rear suspensions, wheels and tires. This decomposition allows us to avoid the complexity accuracy problems derived from the high dimension of required regression space. Indeed, the overall high-dimensional identification problem is reduced to the identification of lower dimensional subsystems and to the estimation of their interactions. An iterative scheme is used for solving the decomposed identification problem. As the chassis pitch is small for the usual road profiles, the chassis-engine block is considered linear and standard linear methods are used for its identification. The other two subsystems are the main sources of nonlinearities in the system, mainly due to the significant nonlinearities of controlled dampers and of tires. Owing to the complexity/accuracy problems of a physical modeling of these subsystems, an input-output approach is taken. In particular, a nonlinear set membership method that does not require the search of the functional form of involved nonlinearities is used for the identification of these subsystems. The iterative algorithm converged in two iterations to a model providing a quite satisfactory simulation accuracy for all the considered road profiles and CDC-Skyhook settings.     

Experimental modeling of controlled suspension vehicles from onboard sensors

In this article, identification of vertical dynamics of vehicles with controlled suspensions is considered. Identification is performed from experimental data measured on a four-poster bench test of a segment C car, equipped with a CDC-Skyhook dampers control system. The measurements are obtained from the onboard accelerometers needed by the control system. A nonlinear model in regression form is identified, having the road profile and damper control currents as inputs and chassis accelerations as outputs. The model is identified by means of a set membership structured identification method, which takes advantage of physical information on the structure of the system, decomposing the system into three subsystems: one represents the chassis and engine and the other two represent the overall behavior of front and rear suspensions, wheels and tires. This decomposition allows us to avoid the complexity accuracy problems derived from the high dimension of required regression space. Indeed, the overall high-dimensional identification problem is reduced to the identification of lower dimensional subsystems and to the estimation of their interactions. An iterative scheme is used for solving the decomposed identification problem. As the chassis pitch is small for the usual road profiles, the chassis-engine block is considered linear and standard linear methods are used for its identification. The other two subsystems are the main sources of nonlinearities in the system, mainly due to the significant nonlinearities of controlled dampers and of tires. Owing to the complexity/accuracy problems of a physical modeling of these subsystems, an input–output approach is taken. In particular, a nonlinear set membership method that does not require the search of the functional form of involved nonlinearities is used for the identification of these subsystems. The iterative algorithm converged in two iterations to a model providing a quite satisfactory simulation accuracy for all the considered road profiles and CDC-Skyhook settings.     

基于RBF神经网络识别路面谱的新方法

路面不平度是车辆行驶中振动的重要激励。为了识别路面不平度的功率谱密度函数(路面谱),提出了一种基于径向基函数(RBF)神经网络识别路面谱的新方法。该方法以7自由度汽车振动模型为基础,以MATLAB软件仿真得到的汽车车身质心垂直加速度谱为神经网络理想输入样本,以GB7031-86建议的路面谱为神经网络理想输出样本,应用RBF神经网络建立汽车车身质心垂直加速度谱和路面谱之间的非线性映射模型。另取一组仿真得到的车身质心垂直加速度谱代入已训练好的网络进行路面谱识别。结果表明:该方法具有较强的抗噪声能力和较理想的识别精度,识别的路面谱与拟合的路面谱吻合一致。     

汽车迟滞非线性动力系统仿真研究

通过轿车副车架橡胶支承的动态特性试验,实现了橡胶支承迟滞非线性特性的数学建模和参数识别。用动态子结构方法将整车模型划分为多个子结构,采用含连接子结构的自由界面模态综合法建立了整车非线性流固耦合模型。用MontoCarlo法模拟路面激励谱和发动机随机激励力谱,利用整车结构-声学耦合系统的运动微分方程,在时域内对路面激励和发动机激励产生的振动和车内噪声特性进行仿真模拟,并通过道路试验和台架试验对计算结果进行了验证。     

考虑未知输入的主动悬架路面高程与等级识别研究

通过主动悬架的精确控制提高车辆乘坐舒适性与行驶安全性的基本前提是进行路面高程与等级识别.本文中设计了考虑未知输入的卡尔曼观测器,以获取路面高程信息;根据路面高程建立AR模型,得到路面功率谱密度,并求取兴趣频段内路面功率谱密度均方根值,实现了路面的等级分类.仿真分析了不同工况下路面高程估计方法和路面等级分类方法的准确性,...