Estimation of the tire slip angle under various road conditions without tire–road information for vehicle stability control

This paper presents a tire slip angle estimator based on an Interacting Multiple Model (IMM) algorithm for vehicle stability control. The proposed algorithm is capable of estimating the tire slip angles under various road conditions without the prior knowledge of tire and road condition by only using on-board vehicle sensors. Instead of employing tire and road information, the proposed algorithm utilizes multiple numbers of model candidates to represent all aspects of vehicle motions under various road conditions. Each model candidate is a combination of lateral vehicle dynamics and transient/steady state tire dynamics. The proposed algorithm evaluates the fidelity of each model candidate to the current vehicle dynamics with probability. Moreover, in the proposed algorithm, multiple numbers of Kalman filters are embedded with these model candidates as process models. The final estimate of the proposed algorithm in each time step is a linear sum of the posteriori states from multiple embedded filters with the calculated probability as coefficients. The proposed estimation algorithm has been evaluated via vehicle tests. The tests have been conducted on dry asphalt and wet asphalt using a luxury passenger car equipped with a high-performance GPS for reference and data logging computer. The results have shown that the proposed estimator can successfully estimate tire slip angles with satisfactory accuracy under various road conditions     

Estimation of road frictional force and wheel slip for effective antilock braking system (ABS) control

The introduction of electric braking via brake‐by‐wire systems in electric vehicles) has reduced the high transportation delays usually involved in conventional friction braking systems. This has facilitated the design of more efficient and advanced control schemes for antilock braking systems (ABSs). However, accurate estimation of the tire‐road friction coefficient, which cannot be measured directly, is required. This paper presents a review of existing estimation methods, focusing on sliding‐mode techniques, followed by the development of a novel friction estimation technique, which is used to design an efficient ABS control system. This is a novel slip‐based estimation method, which accommodates the coupling between the vehicle dynamics, wheel dynamics, and suspension dynamics in a cascaded structure. A higher‐order sliding‐mode observer–based scheme is designed, considering the nonlinear relationship between friction and slip. A first‐order sliding‐mode observer is also designed based on a purely linear relationship. A key feature of the proposed estimation schemes is the inclusion of road slope and the effective radius of the tire as an estimated state. These parameters impact significantly on the accuracy of slip and friction estimation. The performance of the proposed estimation schemes are validated and benchmarked against a Kalman filter (KF) by a series of simulation tests. It is demonstrated that the sliding‐mode observer paradigm is an important tool in developing the next generation ABS systems for electric vehicles.     

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.     

山区道路车辆动力学模型与行车安全分析

针对道路的几何线形,特别是纵坡坡度与弯道半径对车辆行驶状态的影响,建立了车路耦合的8自由度山区道路行驶的车辆动力学模型以及Dugoff轮胎力模型.结合车载GPS/IMU的测量信息,解算了不同车轮的滑移率以及垂直载荷,并通过横向载荷转移率(LLTR)对车辆的行驶稳定性进行分析.结果表明:车辆行驶过程中的侧向加速度与道路纵坡坡度以及车辆重心高度与宽度的比率h/T有关,坡度越陡,h/T越大,侧向加速度越大,车辆的行驶稳定性越差,降低车辆的行驶速度与侧向加速度可提高车辆的行驶稳定性.     

基于滑模观测器的汽车轮胎力级联估计方法

提出了一种新型的基于滑模观测器理论的汽车轮胎力级联估计方法．首先基于单轮滚动动力学模型,以车轮转动角速度及驱动力矩作为输入,针对每个车轮的纵向轮胎力设计了纵向轮胎力滑模观测器．又采用了简化的车辆2自由度模型,以纵向轮胎力估计值、 前轮转角、 侧向加速度及横摆角速度作为输入,分别设计了前、 后轴侧向轮胎力滑模观测器．最后,为验证所设计的观测器的有效性,应用高保真车辆动力学软件veDYNA进行了仿真研究,并与扩展卡尔曼滤波（extendedKalman filter,EKF）方法进行了对比分析．实验结果表明,基于滑模观测器的车辆轮胎力级联估计方法具有更高的准确性．     

非结构道路环境下的智能汽车质心侧偏角估计

车辆质心侧偏角是描述车辆侧向运动状态的重要参量之一,其估计的精度直接影响车辆的安全控制,传统的质心侧偏角估计方法不能满足非结构道路环境下的智能汽车质心侧偏角估计的要求。通过建立3自由度智能汽车动力学模型,采用CarSim和MATLAB构建智能汽车整车参数化模型;基于扩展kalman滤波(EKF)算法,设计非结构道路环境下的状态观测器对智能汽车质心侧偏角进行估计。在高、低附着系数路面双移线工况和蛇形工况下,对状态观测器的估计效果进行联合仿真验证。仿真结果表明:该方法能较精确地估计出非结构道路环境下智能汽车的质心侧偏角。     

Tire lateral force estimation and grip potential identification using Neural Networks,Extended Kalman Filter,and Recursive Least Squares

This paper presents a novel hybrid observer structure to estimate the lateral tire forces and road grip potential without using any tire–road friction model. The observer consists of an Extended Kalman Filter structure, which incorporates the available prior knowledge about the vehicle dynamics, a feedforward Neural Network structure, which is used to estimate the highly nonlinear tire behavior, and a Recursive Least Squares block, which predicts the road grip potential. The proposed observer was evaluated under a wide range of aggressive maneuvers and different road grip conditions using a validated vehicle model, validated tire model, and sensor models in the simulation environment IPG CarMaker ^®. The results confirm its good and robust performance.

     

Security distributed state estimation for nonlinear networked systems against DoS attacks

This paper is concerned with security distributed state estimation for nonlinear networked systems against denial‐of‐service attacks. By taking the effects of resource constraints into consideration, an event‐triggered scheme and a quantization mechanism are employed to alleviate the burden of network. A mathematical model of distributed state estimation is constructed for nonlinear networked systems against denial‐of‐service attacks. Sufficient conditions ensuring the exponential stability of the estimation error systems are obtained by utilizing the Lyapunov stability theory. The explicit expressions of the designed state estimators are acquired in terms of the linear matrix inequalities. Finally, a numerical example is used to testify the feasibility of the proposed method.     

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.     

太阳能赛车能量流建模仿真和实验研究

为制定太阳能赛车高效能量管理策略,以太阳能赛车为研究对象,建立考虑光伏电池温度、空气密度、胎压、悬架阻尼等时变参数的整车模型,设计了基于带遗忘因子的递推最小二乘法在线道路坡度估计算法,建立了整车MATLAB/Simulink模型,仿真研究了巡航速度、整车质量和轮胎压力参数变化对于整车能耗特性的影响,并进行了实验验证。实验结果表明上述模型能够真实反应车辆和环境参数变化对于整车能耗的影响,为后续太阳能赛车能量策略的制定奠定了基础。     

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.     

Estimation of automotive tire force characteristics using wheel velocity

It is important to estimate friction force characteristics between tire and road in order to improve the control performance of a vehicle in critical motions. In this paper, an estimation method which estimates parameters concerned with a friction force margin is proposed by applying the on-line least-squares method to wheel rotational velocities. The effect of the estimation is evaluated by applying the method to the braking control.     

自动驾驶在拥堵路段的道路几何信息估计

道路几何信息是自动驾驶系统中重要的信息来源,也是后续路径规划的关键参考信息之一。 该研究针对城市内车道线遮挡及多路径效应导致的全球定位系统失效等问题,提出了一种基于前车信 息的道路几何估计方法。通过对当前车辆、前车以及道路之间关系的建模,获得了系统的运动模型和 观测模型。采用无损卡尔曼滤波框架对观测到的前车相对位置、相对速度、相对角度和本车角速度进 行滤波处理,估计出当前车道的曲率参数。在仿真软件 Car learning to Act(Carla)上的实验结果表明, 相比地图匹配方法,在无法获取车道线目标及精确定位信息的情况下,该方法道路几何精度得到了显 著提升。     

Vehicle sideslip estimation: A kinematic based approach

This paper deals with vehicle sideslip angle estimation. The paper introduces an industrially amenable kinematic-based approach that does not need tire–road friction parameters or other dynamical properties of the vehicle. The convergence of the estimate is improved by the introduction of a heuristic based on readily available inertial measurements. The method is tested on a vast collection of tests performed in different conditions, showing a satisfactory behavior despite not using any information on the road friction. The extensive experimental validation confirms that the estimate is robust to a wide range of driving scenarios.     

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.     

Estimation of Road Friction Coefficient and Vehicle States by 3‐DOF Dynamic Model and HSRI Model Based on Information Fusion

Vehicle states and the road friction coefficient in active safety control systems have become increasingly prominent. However, a low‐cost, high‐precision system in real‐time has yet to be achieved. The use of complex models has led to poor real‐time estimation, while variations in the road friction coefficient have often been neglected. This paper adopts information fusion technology by using DEKF theory for rapid simulation and estimation of these parameters. Using a vehicle dynamic model based on three degrees of freedom (3‐DOF) and the Highway Safety Research Institute tire model, DEKF recursive estimation models are established and verified. In the DEKF, two recursive state and parameter estimation models exist in parallel. The models are dependent upon each other and have real‐time interaction correction in order to forecast information, which quickly yields true value estimation in simulation. Experimental brake test results show that the DEKF estimator not only accurately estimates the vehicle state parameters, but also estimates the road friction coefficient in real‐time. This can reduce the cost of the vehicle sensor, and can estimate the status parameter, which is difficult to measure. The validity and feasibility of this algorithm have been verified by an HIL driving simulator, offering the possibility of future application in real cars.     

Moving horizon estimation for switching nonlinear systems

This paper is concerned with moving horizon estimation for a class of constrained switching nonlinear systems, where the system mode is regarded as an unknown discrete state to be estimated together with the continuous state. In this work, we establish the observability framework of switching nonlinear systems by proposing a series of concepts about observability and analyzing the properties of such concepts. By fully applying the observability properties, we prove the stability of the proposed moving horizon estimators. Simulation results are reported to verify the derived results.     

Nonlinear vehicle side-slip estimation with friction adaptation

A nonlinear observer for estimation of the longitudinal velocity, lateral velocity, and yaw rate of a vehicle, designed for the purpose of vehicle side-slip estimation, is modified and extended in order to work for different road surface conditions. The observer relies on a road-tire friction model and is therefore sensitive to changes in the adhesion characteristics of the road surface. The friction model is parametrized with a single friction parameter, and an update law is designed. The adaptive observer is proven to be uniformly globally asymptotically stable and uniformly locally exponentially stable under a persistency-of-excitation condition and a set of technical assumptions, using results related to Matrosov's theorem. The observer is tested on recorded data from two test vehicles and shows good results on a range of road surfaces.     

网联车辆协同巡航系统快速滚动时域状态估计

考虑云平台监控下的网联车辆协同自动巡航控制(CACC)系统,提出一种快速滚动时域估计方法.采用网联车队纵向动力学模型描述网联车辆CACC系统,降低网联车辆CACC系统的状态能观性要求.再应用块概念设计滚动时域估计算法的噪声块结构,压缩滚动时域估计问题的优化变量个数,从而减少其在线计算量.进一步,应用李雅普诺夫稳定性定理...     

基于并行学习鲁棒自适应的行驶车辆特性参数估计方法研究

针对车辆行驶过程中的特性参数估计问题,基于并行学习思想提出一种鲁棒自适应参数估计方法.通过低通滤波技术,设计一组系统状态和响应函数的一阶滤波变量.结合并行学习,构建特性参数估计的回归向量,并基于参数估计误差向量,设计鲁棒自适应参数更新律.以某型车辆为例,对该方法的有效性进行仿真验证.仿真结果表明,在无/有扰动情形下,该...