汽车道路模拟系统特性辨识研究

在进行室内汽车道路模拟试验时,对由振动台架和被试对象组成的试验系统,准确地辨识出系统特性是实验取得成功的关键步骤之一。详细论述了利用CARMA模型识别道路模拟试验系统的原理和过程。基于单输入单输出系统的时域CARMA模型．通过优化算法和系统辨识的方法识别了模型参数及模型阶次,并通过试验验证了这种方法的正确性。     

用系统辨识的方法辨识发动机调速系统的模型

本文用系统辨识的方法建立发动机调速系统的等效线性模型。辨识实验中所采用的输入信号是伪随机二进制序列，参数估计中所采用的方法是最小二乘递推估计算法，模型阶次的检验是采用了Ｆ检验方法，辨识所得到的效线性模型是有两个比例环节和一个积分环节的闭环系统。     

重型载货汽车气压制动系统危险状态识别

针对重型载货汽车因气压制动系统发生管路破裂、机械故障或热衰退导致制动效能下降且不易察觉从而引发严重交通事故的问题，提出基于主成分分析降维（PCA降维）和马尔可夫模型的气压制动系统危险状态识别方法。考虑到三轴载货汽车双回路制动系统的结构复杂性以及制动过程制动踏板动作、系统压力建立和实现车辆减速具有明显的时序性特点，首先采用PCA降维的方法对系统状态进行辨识；然后运用驾驶人制动意图与制动系统响应的双层隐形马尔可夫模型对系统状态进行识别。受驾驶人习惯影响制动踏板作用瞬间辨识度低，采用混合高斯聚类法提取不同制动意图时制动保持阶段数据建立制动意图识别模型和系统响应识别模型，通过二者匹配程度判定系统状态。最后，分别依据实车试验数据对模型进行离线训练和在线辨识验证。试验结果表明：系统正常状态下，基于PCA降维和马尔可夫模型相结合的识别方法能够准确、有效地识别制动系统状态；制动管路断开压力降低状态下，PCA降维方法能够及时有效识别其危险状态。     

基于频率加权左互质分解的汽车悬架H∞控制器降阶研究

建立了7自由度汽车整车悬架模型,考虑人体对垂直和旋转方向振动的敏感频率范围,设计频率加权的H∞控制器。进而在选取特定加权矩阵保证降阶闭环系统稳定前提下,应用基于频率加权左互质分解的控制器降阶方法,对所设计的高阶H∞控制器进行降阶。仿真结果表明,控制器的阶数能够被较大程度地降低,而闭环控制效果损失很小,证明了频率加权左互质分解控制器降阶方法的有效性。     

汽车防抱制动过程仿真计算模型及其参数的系统辨识

本文根据系统辨识理论和防抱制动装置工作原理，建立汽车制动ＡＲＭＡ模型和防抱制动装置仿真计算模型，并根据防抱制动试验中获得的数据对所建立的参数进行辨识，最后的仿真计算结果与试验吻合良好，表明系统辨识方法是分析汽车防抱制动过程的有效手段。     

采用最小信息损失方法的汽车主动悬架控制器降阶研究

针对汽车悬架系统控制器阶数高、工程上难以实现的问题,建立了整车7自由度主动悬架模型,考虑人体对振动的敏感频段,设计了H∞加权控制器,以保证闭环系统稳定且具有较好的抗干扰性能.基于最小信息损失方法对20阶主动悬架控制器进行降阶研究.通过对降阶前后主动悬架闭环控制系统频域特性和乘坐舒适性的对比表明,将20阶主动悬架控制器降至8阶后,控制器状态的能控与能观信息损失之和小于50％,且悬架控制系统仍具有十分相近的控制性能.     

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。     

基于自动驾驶汽车稳定性的聚类辨识模型设计

在自动驾驶汽车中高级辅助驾驶系统（ADAS）的设计过程中，车辆稳定性控制目标并没有考虑驾驶员个性化特质需求，尤其在一些极端行驶条件下控制效果会适得其反。鉴于此，在传统汽车稳定性评价标准的基础上融合了隐马尔科夫理论（HMM）和K-means聚类算法，采用无迹卡尔曼滤波和因子加权分析的参数处理方法，设计了一种自动驾驶汽车稳定性辨识模型。模型通过Carsim/Simulink和基于DSPACE驾驶模拟器的硬件在环仿真方法进行了验证。结果表明：该模型能够实现自动驾驶汽车稳定性的合理分类和在线辨识，同时能为今后进一步优化自动驾驶汽车轨迹规划方法提供理论依据。     

采用响应面法的汽车转向系统固有频率优化

提出一种基于响应面方法的汽车转向系统固有频率优化方法。以某微型车为例,该方法从建立汽车转向系统的有限元模型出发,结合拉丁方试验设计方法,采用最小二乘法建立转向系统的二次响应面近似模型;在此基础上,以转向系统的1阶固有频率最大化为目标,总质量为约束,并通过灵敏度分析选取5个部件有限元模型壳单元壁厚为设计参数,建立优化模型。对转向系统进行优化的结果表明,采用该方法对汽车转向系统进行优化,能有效提高其1阶固有频率,减小转向系统质量,从而抑制转向系统的振动。     

汽车电动转向系统缺陷辨识实验方法的研究

鉴于汽车转向系统的缺陷严重影响行车安全,本文在分析汽车转向系统缺陷形式及其影响的基础上,提出了针对电动助力转向系统的缺陷(包括助力消失、助力过大/不足和误助力等缺陷)的实验辨识方法,并通过台架实验进行了验证。实验结果表明,所提出的实验辨识方法可有效辨识电动助力转向系统的主要缺陷。     

Identification Methods for Vehicle System Dynamics

The paper presents a survey on parameter identification techniques for complex vehicle models. In order to cope with the complexity of the model, the information on the system available from the equations of motion has to be included in the identification process. Basic methods for the solution of this problem are shown. The application of the approach is demonstrated by identification of the vertical automobile dynamics. It is concluded that the presented techniques will become more important with increasing applications of theoretical modeling in vehicle system dynamics.     

Load and Load Dependent Friction Identification and Compensation of Electronic Non-Circular Gear Brake System

Control of the electronic non-circular gear brake (ENGB) involves challenges, including the non-linear variation of loads and the effect of friction, which is dependent upon load. The controller must be designed based on modelling information in order to enhance control performance. This study performed model identification of the ENGB system using a DOB-based model identification method. By employing the nearest neighbor search method, the even-odd disturbance was separated without the influence of hysteresis even in situations with low control precision. The accuracy of the resulting ENGB system model was validated through experiments. The self-energizing effect due to friction between the brake disc and pad within the mechanical system was also validated.     

Vehicle handling dynamics modelling by a data-driven identification method

Modelling of vehicle handling dynamics has received a renewed attention in recent years. Different from traditional vehicle modelling, a novel data-driven identification method for vehicle handling dynamics is proposed, which can avoid the problems of the under-modelling and parameter uncertainties in the first-principle modelling process. By first-order Taylor expansion, the nonlinear vehicle system can be linearised as a slowly linear time-varying system with fourth-order. In order to identify the derived identifiable model structure, a recursive subspace method is presented. Derived by optimal version of predictor-based subspace identification (PBSID_opt) and projection approximation subspace tracking (PAST), the identification method is numerical stability and gives an unbiased estimation for the closed-loop system. Based on standard road tests, the proposed modelling method is proven effective and the obtained model has good predictive ability. Additionally, it is noted that the model obtained from the initial phase of straight driving is just a mathematical model to describe the relationship between input and output. And when the vehicle is steering, the model can converge to a stable phase quickly and represent vehicle dynamic performance.     

Identification Methods for Vehicle System Dynamics

SUMMARY

The paper presents a survey on parameter identification techniques for complex vehicle models. In order to cope with the complexity of the model, the information on the system available from the equations of motion has to be included in the identification process. Basic methods for the solution of this problem are shown. The application of the approach is demonstrated by identification of the vertical automobile dynamics. It is concluded that the presented techniques will become more important with increasing applications of theoretical modeling in vehicle system dynamics.     

数据驱动随机子空间算法的桥梁运营模态分析

以某自锚式悬索桥模型试验为研究背景,采用数据驱动随机子空间识别算法和改进稳定图方法对桥梁结构运营模态分析进行研究.为解决数据驱动随机子空间识别中的系统定阶和虚假模态问题,采用奇异熵增量进行系统定阶,并对稳定图进行改进,实现了虚假模态的识别与剔除,最终达到了精确识别桥梁结构模态参数的目的.采用模型试验在不同数据采集方案下的测试数据,识别该模型桥相应测试条件下的模态参数,将识别结果分别与ANSYS理论计算值、DASY-Lab模态参数识别结果进行比较,验证了所提方法及自编程序的正确性,该方法可应用于桥梁结构的运营模态分析中.     

Adaptive feedforward control of a steer-by-wire system by online parameter estimator

The tracking control of the steer-by-wire (SBW) system to achevie desired steering motion is the core issue for the design of algorithm. Most of model-based tracking control assumed the constant parameters without the consideration of dynamic characteristics. The external disturbances and model nonlinearities can bring uncertainties of the system parameters. To reduce the influence of parameter uncertainties, an online estimator by output error identification method is proposed to estimate the dynamic parameters of a SBW system. Meanwhile, the parameter gradient projection method is applied to eliminate the parameter drift, while a full order state observer is developed to weaken the effects of noise disturbance during the parameter identification. Since the sensitivity of parameter uncertainties for the feedforward control, the online estimator is incorporated into the control model and improve the controlled robustness. The proposed adaptive feedforward controller is conducted by the real-time experiments to show the tracking performance.     

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.     

Lateral Dynamics of an Offroad Motorcycle by System Identification

An instrumented offroad motorcycle was run at a range of speeds in approximately straight lines whilst the rider excited its lateral dynamics by shaking the steering. Autoregressive models were fitted to the resulting multiple output time series data using system identification. The method allowed statistical estimation of state space models to represent the dynamics of an unstable or marginally stable vehicle under manual control. A symbolic algebra computer package was used to derive an analytical state space model to describe the lateral dynamics of the motorcycle. Results from the experiments and analysis compared well with respect to frequency, damping and modal shape of weave and wobble modes, frequency response and model order.     

智能交通系统中基于机器视觉的数字车辆控制

在描述成像模型与视觉坐标系的基础上，给出了基于计算机视觉的车道与障碍物辨识检测算法，应用预瞄转向以及车辆控制等技术手段来实现数字车辆的道路跟踪。