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.     

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.     

Vehicle velocity estimation using nonlinear observers

Nonlinear observers for estimation of lateral and longitudinal velocity of automotive vehicles are proposed. The observers are based on a sensor suite that is standard in many new cars, consisting of acceleration and yaw rate measurements in addition to wheel speed and steering angle measurements. Two approaches are considered: first, a modular approach where the estimated longitudinal velocity is used as input to the observer for lateral velocity, and second, a combined approach where all states are estimated in the same observer. Both approaches use a tire-road friction model, which is assumed to be known. It is also assumed that the road is flat. Stability of the observers is proven in the form of input-to-state stability of the observer error dynamics, under a structural assumption on the friction model. The assumption on the friction model is discussed in detail, and the observers are validated on experimental data from cars.     

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。     

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.     

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 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.     

Non-linear velocity observer for vehicles with tyre–road friction estimation

A non-linear observer is proposed for the estimation of the longitudinal and lateral velocities of automotive vehicles due to highly non-linear friction. To take the unknown time-varying road conditions into account, a real-time tyre–road friction estimation algorithm is provided for adaptation to changes of different road adhesion characteristics. Besides, the coupling effects of the longitudinal and lateral forces are fully exploited to design the non-linear observer based on the longitudinal and lateral acceleration measurements. The observer is computationally efficient and is based on a standard sensor configuration commonly available in modern cars. The uniform global asymptotical stability of the adaptive observer is guaranteed under a certain persistency-of-excitation condition. Moreover, a stronger local stability result can also be obtained, i.e. uniform local exponential stability. The performance of the observer is compared with that of existing approaches under different manoeuvres and road conditions.     

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．     

防抱制动系统滑模状态观测和控制系统仿真

该文在考虑不平路面随机激励作用下车辆垂向振动的基础上 ,首先建立了四分之一车辆制动模型 ,而后充分运用滑移模式变结构的分析和设计方法 ,提出了车轮最佳滑移率的滑模实时在线辨识滑模优化算法 ,在对系统可观测性论证的基础上 ,设计了非线性滑模状态观测器 ,给出了单通道防抱制动系统基于滑移率的滑模控制算法 ,通过计算机仿真 ,验证了该控制算法的可行性和有效性 ,为设计具有高鲁棒性的防抱制动系统做了一定的理论探索和仿真工作     

Observer design for sensor and actuator fault estimation applied to polynomial LPV systems: a riderless bicycle study case

The present paper addresses an observer design for fault estimation applied to polynomial linear parameter varying (LPV) systems. The main contribution corresponds to a design to provide the estimation of both actuator and sensor faults acting on the system, modelled as additive faults taking place at any time and simultaneously, inclusively. The observer design includes a boundary factor in order to guarantee the estimation error convergence to zero despite the fault occurrence. Simulation results showing the effectiveness of the proposed observer by considering a riderless bicycle LPV model, which depends on the translational velocity of the vehicle, are provided.     

Output Feedback Dynamic Surface Controller Design for Airbreathing Hypersonic Flight Vehicle

This paper addresses issues related to nonlinear robust output feedback controller design for a nonlinear model of airbreathing hypersonic vehicle. The control objective is to realize robust tracking of velocity and altitude in the presence of immeasurable states, uncertainties and varying flight conditions. A novel reduced order fuzzy observer is proposed to estimate the immeasurable states. Based on the information of observer and the measured states, a new robust output feedback controller combining dynamic surface theory and fuzzy logic system is proposed for airbreathing hypersonic vehicle. The closedloop system is proved to be semi-globally uniformly ultimately bounded (SUUB), and the tracking error can be made small enough by choosing proper gains of the controller, filter and observer. Simulation results from the full nonlinear vehicle model illustrate the effectiveness and good performance of the proposed control scheme.      

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。     

A diagnosis-based approach for tire-road forces and maximum friction estimation

A new approach to estimate vehicle tire forces and road maximum adherence is presented. Contrarily to most of the previous works on this subject, it is not an asymptotic observer-based estimation, but a combination of elementary diagnosis tools and new algebraic techniques for filtering and estimating derivatives of noisy signals. In a first step, instantaneous friction and lateral forces will be computed within this framework. Then, extended braking stiffness concept is exploited to detect which braking efforts allow to distinguish a road type from another. A weighted Dugoff model is used during these ‘distinguishable’ intervals to estimate the maximum friction coefficient. Very promising results have been obtained in noisy simulations and real experimentations for most of the driving situations.     

基于神经网络观测器的船舶轨迹跟踪递归滑模 动态面输出反馈控制

针对三自由度全驱动船舶速度向量不可测问题,考虑船舶模型参数和外部环境扰动均未知的情况,提出一种基于神经网络观测器的船舶轨迹跟踪递归滑模动态面输出反馈控制方法.该方法设计神经网络自适应观测器估计船舶速度向量,且利用神经网络逼近模型参数不确定项,综合考虑船舶位置和速度误差之间关系构造递归滑模面,再采用动态面控制技术设计轨迹跟踪控制律和参数自适应律,并引入低频增益学习方法消除外界扰动导致的高频振荡控制信号.选取李雅普诺夫函数证明了该控制律能够保证轨迹跟踪闭环系统内所有信号的一致最终有界性.最后,基于一艘供给船进行仿真验证,结果表明,船舶轨迹跟踪响应速度快,所设计控制器对系统模型参数摄动及外界扰动具有较强的鲁棒性.     

含有摩擦补偿的全方位移动机器人自抗扰控制

本文针对全方位移动机器人轨迹追踪中的摩擦补偿问题,提出了一种改进的非线性自抗扰控制器.首先建立了含有经典静态摩擦模型的全方位移动机器人动力学模型.其次,基于该模型设计非线性控制器和线性扩张状态观测器并给出了系统的稳定性分析.通过将模型已知项加入线性扩张状态观测器中得到摩擦力的估计值,并将估计值用于非线性控制器中摩擦补偿部分.为减小摩擦力对机器人低速运动轨迹追踪控制的影响,非线性控制器采用变增益控制器进行轨迹追踪控制.最后通过仿真结果验证本文提出控制器的有效性.     

基于道路摩擦系数估计的智能小车紧急避障策略研究

雾霾环境下驾驶员的视野受到限制,无法准确估计周围的环境信息,对行车安全具有重大影响.自主紧急制动(AEB)系统是一种重要的车辆主动安全功能,用来避免碰撞或减轻碰撞程度.通常,AEB系统利用一个碰撞时间TTC衡量与障碍物发生碰撞的危险程度.通常设计用于制动的TTC门槛值时假设道路摩擦系数为常数,然而,道路情况复杂多变,道路摩擦系数也是变化的,驾驶员在雾霾环境下更难准确估计道路摩擦系数.因此,开发了一个考虑不同摩擦系数对TTC门槛值影响的AEB控制策略.首先用一个复合滑移率轮胎模型来估计峰值道路摩擦系数,再用该系数计算TTC的门槛值,进而利用该Trc门槛值衡量与障碍物发生碰撞的危险程度.因为可以实时识别道路摩擦系数,提出的AEB策略可以自适应雾霾环境下不同的道路表面.仿真结果表明了该方法的有效性.     

Magnetohydrodynamic state estimation with boundary sensors

We present a PDE observer that estimates the velocity, pressure, electric potential and current fields in a magnetohydrodynamic (MHD) channel flow, also known as Hartmann flow. This flow is characterized by an electrically conducting fluid moving between parallel plates in the presence of an externally imposed transverse magnetic field. The system is described by the inductionless MHD equations, a combination of the Navier-Stokes equations and a Poisson equation for the electric potential under the so-called inductionless MHD approximation in a low magnetic Reynolds number regime. We identify physical quantities (measurable on the wall of the channel) that are sufficient to generate convergent estimates of the velocity, pressure, and electric potential field away from the walls. Our observer consists of a copy of the linearized MHD equations, combined with linear injection of output estimation error, with observer gains designed using backstepping. Pressure, skin friction and current measurements from one of the walls are used for output injection. For zero magnetic field or nonconducting fluid, the design reduces to an observer for the Navier-Stokes Poiseuille flow, a benchmark for flow control and turbulence estimation. We show that for the linearized MHD model the estimation error converges to zero in the L² norm. Despite being a subject of practical interest, the problem of observer design for nondiscretized 3-D MHD or Navier-Stokes channel flow has so far been an open problem.     

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.