一种新型的轮胎力级联估计方法

为了增强车辆主动安全性和底盘的集成控制,根据干扰输入观测器理论,基于简化的车轮动力学模型设计估计器,对每个轮胎纵向力进行估计.此外,基于"自行车"模型,利用车辆动力学,在纵向轮胎力估计值的基础上估计轮胎侧向力.涉及到的车载传感器测量值包括发动机扭矩和转速、轮缸压力、车轮角速度、方向盘转角、侧向加速度和横摆角速度.为验证轮胎力级联估计方法的有效性,应用高保真的车辆动力学软件ve DYNA进行了仿真研究,并与扩展卡尔曼滤波(extended Kalman filter,EKF)方法作对比分析.实验结果表明,轮胎力级联估计方法能够获得车辆轮胎力的估计效果.     

基于Uni-Tire轮胎模型的车辆质心侧偏角估计

针对车辆质心侧偏角估计的准确性和实时性能问题,提出了车辆质心侧偏角估计的非线性全维观测器设计方法.首先基于车辆动力学模型及纵滑-侧偏联合工况下的Uni-Tire轮胎模型,利用车载传感器测量车辆状态;观测器利用这些状态估计出车辆的纵向速度、侧向速度及横摆角速度,并由此得到车辆的质心侧偏角估计.其次利用输入-状态稳定(input-to-state stability,ISS)理论对观测器的稳定性进行了分析.最后采用红旗CA7180A3E型轿车的车辆参数使用车辆仿真软件veDYNA对极限工况下的估计结果进行了离线仿真研究,并利用xPC-Target仿真环境和dSPACE实时仿真系统搭建仿真平台,对非线性全维观测器的实时性进行验证.仿真结果表明,非线性估计方法估计精度较高,实时性较好,可以满足工程应用的要求.     

基于滑模观测器的车辆电子稳定性控制系统故障重构

针对车辆电子稳定性控制系统的横摆角速度传感器和侧向加速度传感器故障检测和重构问题,使用T-S模糊系统建立了车辆动力学系统的全局模型,依据滑模控制理论,给出了基于滑模观测器的传感器故障检测和重构方法,且所设计观测器满足给定的从未知输入到故障重构误差的L2增益性能要求.最后通过实测数据,验证了方法是可行的.     

分布式汽车驱动力能量效率优化分配控制

充分利用分布式驱动汽车信息源多的特点,根据扩展卡尔曼滤波算法（EKF）建立观测器对车轮侧向力进行在线估计。通过改进的车辆线性二自由度模型制定系统控制目标,依据车轮侧向力观测值设计了基于滑模变结构控制的直接横摆力矩控制器。全轮驱动力综合优化分配策略同时考虑了轮胎负荷率与驱动电机效率,完成了对车轮稳定性与能量效率的耦合控制。通过Carsim-Matlab/Simulink的仿真表明,整个系统实现了对车轮侧向力的准确估计,提高了目标直接横摆力矩计算的准确性。驱动力综合优化分配在提高车辆路面附着余量的同时也提高了各驱动电机的综合效率,进一步提高了车辆的能量利用效率。     

车辆稳定控制中的合力计算与分配

控制地面作用于车辆的纵侧向合力与横摆转矩并将其分配到4个车轮,是车辆平面运动稳定控制的方法之一.路面的附着极限决定了合力与横摆转矩的可行域,是该方法的约束条件.本文分析了轮胎的受力特点,在摩擦椭圆理论的基础上,给出了简化的纵侧向力耦合关系.利用非线性规划方法和大量的数值计算,解决纵向合力与横摆转矩可行域的实时估计问题.构造了一种控制结构,外环控制器计算可行的纵向合力与横摆转矩;内环控制器首先将纵向合力优化分配到4个车轮,然后通过调节前轮转角使横摆转矩跟踪期望值.仿真结果表明,采用本文提出的方法对车辆进行控制,能够实现横摆角速度的快速准确跟踪,并使车辆具有良好的操纵稳定性.     

基于信息融合技术的车辆行驶状态估计

针对车辆行驶状态估计问题,基于扩展卡尔曼滤波(EKF)理论设计了汽车横摆角速度、质心侧偏角和纵向车速的估计算法.建立了车辆非线性三自由度估算模型,应用扩展卡尔曼滤波对纵向加速度、侧向加速度和方向盘转角低成本传感器信号的信息融合,实现对车辆行驶状态的准确估计.选择高速双移线工况,通过CarSim与Matlab/Simulink联合仿真对算法进行仿真验证.结果表明:算法能够准确估计车辆的横摆角速度、质心侧偏角和纵向车速,并具有良好的实时性.     

汽车横摆的动态模型

建立横摆动态模型是汽车横摆稳定控制的基础．本文根据汽车横摆稳定控制的特点,综合车身横摆动态,车轮动态和轮胎的非线性特性,建立了非线性离散横摆动态模型,其中轮胎摩擦力为时变参数．为了估计本模型中的时变参数,本文进而设计了离散滑模估计器估计轮胎摩擦力．最后,在一个高精度的汽车动力学仿真环境中,证实了本估计方法可有效地估计车轮摩擦力,所建立模型较为准确地反映汽车横摆动态．     

高速转向工况下的无人驾驶车辆路径跟踪

为提高无人驾驶车辆在高速转向工况下的路径跟踪精度与行驶稳定性,基于三自由度单轨车辆模型与模型预测控制理论,分析前轮转角约束对车辆跟踪精度与行驶稳定性的影响,提出一种自适应于侧向附着力的路径跟踪控制方法.以Pacejka'89魔术公式轮胎模型为基础,分析轮胎纵向受力,以此推算轮胎的侧向附着力,从而建立前轮转角约束随车辆状...     

一种提高无人车在弯道时操纵稳定性的算法

针对车辆弯道的行驶控制中,由于车辆模型维数高、强耦合及非线性强等特点,常规控制算法稳定性差.在深入分析车辆动力学的基础上,简化并建立了其整车动力学及轮胎动力学模型,以行车轨迹与预瞄轨迹的偏差作为输入,提出了一种基于偏差的纵向减速与横向TS-PD相结合的控制算法,实现车辆的弯道自动变速跟随预瞄轨迹的控制目标.仿真结果表明:上述算法与常规控制算法相对比,系统跟踪误差由1m缩小至0.2m,车辆前轮输出更为平滑,具有较强的鲁棒性.     

基于Lyapunov函数方法的时滞车辆纵向跟随控制

应用向量Lyapunov函数方法和比较原理,基于非线性车辆动态耦合模型,研究具有时间滞后的车辆跟随系统的指数稳定性问题,得到了车辆跟随系统的指数稳定性判据．根据滑模控制策略确定了车辆跟随系统的纵向控制规律,基于稳定性准则设计了车辆纵向跟随控制器参数．仿真结果表明,基于该方法设计的车辆纵向跟随控制器能使跟踪误差具有较快的收敛率．     

Estimation of vehicle sideslip, tire force and wheel cornering stiffness

This paper presents a process for the estimation of tire–road forces, vehicle sideslip angle and wheel cornering stiffness. The method uses measurements (yaw rate, longitudinal/lateral accelerations, steering angle and angular wheel velocities) only from sensors which can be integrated or have already been integrated in modern cars. The estimation process is based on two blocks in series: the first block contains a sliding-mode observer whose principal role is to calculate tire–road forces, while in the second block an extended Kalman filter estimates sideslip angle and cornering stiffness. More specifically, this study proposes an adaptive tire-force model that takes variations in road friction into account. The paper also presents a study of convergence for the sliding-mode observer. The estimation process was applied and compared to real experimental data, in particular wheel force measurements. The vehicle mass is assumed to be known. Experimental results show the accuracy and potential of the estimation process.     

Vehicle state estimation for anti-lock control with nonlinear observer

Vehicle state estimation during anti-lock braking is considered. A novel nonlinear observer based on a vehicle dynamics model and a simplified Pacejka tire model is introduced in order to provide estimates of longitudinal and lateral vehicle velocities and the tire-road friction coefficient for vehicle safety control systems, specifically anti-lock braking control. The approach differs from previous work on vehicle state estimation in two main respects. The first is the introduction of a switched nonlinear observer in order to deal with the fact that in some driving situations the information provided by the sensor is not sufficient to carry out state estimation (i.e., not all states are observable). This is shown through an observability analysis. The second contribution is the introduction of tire-road friction estimation depending on vehicle longitudinal motion. Stability properties of the observer are analyzed using a Lyapunov function based method. Practical applicability of the proposed nonlinear observer is shown by means of experimental results.     

Structured estimation of tire forces and the ground slope using SM observers

In agricultural context, the principal cause of serious accidents for all-terrain vehicles(ATVs) is rollover. The most important parameters related to this risk is the ground slope. In this paper, we propose a structured observer to estimate the system states and the longitudinal tire forces using only wheel angular velocities measurement. The robust estimation is based on a second order sliding mode observer. This estimation is then used to build up a ground slope estimation. The algorithm is composed by two cascaded estimators. This structured estimation is then applied to the model of an agricultural vehicle G7(GregoireTM) integrated in the driving simulation environment SCANeRTM-Studio.     

基于轮胎/路面磨擦状况估计的轮胎故障观测器设计

轮胎故障是造成交通事故的主要原因之一.但是目前大多数轮胎故障监测方法由于需 要使用各种复杂的传感器因此制造代价高昂且不可靠.为此,提出了一种新型实用的轮眙故障观 测器.基于考虑外界不确定干扰的新型动态轮胎/路面磨擦模型,该观测器仅仅使用汽车驱动力 及轮胎转速数据,跟踪估计轮胎/路面磨擦系数的变化,并通过对磨擦状况的分析对轮胎状态做 出合理的判断.由于转速传感器是汽车防滑刹车控制系统(ABS)的基本组成部分,因此该观测器 可与ABS结合工作,低成本的实现轮胎故障监测.     

Detection of Impending Vehicle Rollover with Road Bank Angle Consideration Using a Robust Fuzzy Observer

 This article describes a method of vehicle dynamics estimation for impending rollover detection. We estimate vehicle dynamic states in presence of the road bank angle as a disturbance in the vehicle model using a robust observer. The estimated roll angle and roll rate are used to compute the rollover index which is based on the prediction of the lateral load transfer. In order to anticipate rollover detection, a new method is proposed to compute the time to rollover (TTR) using the load transfer ratio (LTR). The nonlinear model, deduced from the vehicle lateral and roll dynamics, is represented by a Takagi-Sugeno (T-S) fuzzy model. This representation is used to account for the nonlinearities of lateral cornering forces. The proposed T-S observer is designed with unmeasurable premise variables to cater for non-availability of the slip angles measurement. The proposed approach is evaluated using CarSim simulator under different driving scenarios. Simulation results show good efficiency of the proposed T-S observer and the rollover detection method.     

动力定位船舶自适应滑模无源观测器设计

针对带有模型参数不确定性的动力定位船舶,提出一种动力定位船全速域自适应滑模无源观测器,解决了现有观测器只能应用于低速作业动力定位系统的问题.采用速度估计误差作为滑模面,设计切换自适应律估计模型不确定项上界,保证了观测器增益的有界性和系统鲁棒性.对速度估计回路的无源性进行了分析,并证明了观测器的稳定性.最后利用船舶动力定位系统半实物仿真平台,验证了算法的有效性.     

Tire–road friction coefficient and tire cornering stiffness estimation based on longitudinal tire force difference generation

A sequential tire cornering stiffness coefficient and tire–road friction coefficient (TRFC) estimation method is proposed for some advanced vehicle architectures, such as the four-wheel independently-actuated (FWIA) electric vehicles, where longitudinal tire force difference between the left and right sides of the vehicle can be easily generated. Such a tire force difference can affect the vehicle yaw motion, and can be utilized to estimate the tire cornering stiffness coefficient and TRFC. The proposed tire cornering stiffness coefficient and TRFC identification method has the potential of estimating these parameters without affecting the vehicle desired motion control and trajectory tracking objectives. Simulation and experimental results with a FWIA electric vehicle show the effectiveness of the proposed estimation method.     

Identification of vehicle parameters and estimation of vertical forces

The aim of the present work is to estimate the vertical forces and to identify the unknown dynamic parameters of a vehicle using the sliding mode observers approach. The estimation of vertical forces needs a good knowledge of dynamic parameters such as damping coefficient, spring stiffness and unsprung masses, etc.

In this paper, suspension stiffness and unsprung masses have been identified by the Least Square Method.

Real-time tests have been carried out on an instrumented static vehicle, excited vertically by hydraulic jacks. The vehicle is equipped with different sensors in order to measure its dynamics. The measurements coming from these sensors have been considered as unknown inputs of the system. However, only the roll angle and the suspension deflection measurements have been used in order to perform the observer. Experimental results are presented and discussed to show the quality of the proposed approach.     

Corner-based estimation of tire forces and vehicle velocities robust to road conditions

Recent developments in vehicle stability control and active safety systems have led to an interest in reliable vehicle state estimation on various road conditions. This paper presents a novel method for tire force and velocity estimation at each corner to monitor tire capacities individually. This is entailed for more demanding advanced vehicle stability systems and especially in full autonomous driving in harsh maneuvers. By integrating the lumped LuGre tire model and the vehicle kinematics, it is shown that the proposed corner-based estimator does not require knowledge of the road friction and is robust to model uncertainties. The stability of the time-varying longitudinal and lateral velocity estimators is explored. The proposed method is experimentally validated in several maneuvers on different road surface frictions. The experimental results confirm the accuracy and robustness of the state estimators.