New adaptive approaches to real-time estimation of vehicle sideslip angle

This paper presents new approaches to the identification of the vehicle sideslip angle and the road bank angle in real-time. The major challenge is that the vehicle sideslip angle and the road bank angle are coupled together with the system uncertainties, such as variations in the vehicle parameters and the tire cornering stiffness. To resolve this difficulty, the proposed estimation algorithms identify the uncertain vehicle parameters using the sensor measurements such as the steering angle, the lateral acceleration and the yaw rate, and then estimate the vehicle sideslip angle and the road bank angle via a simple algebraic relationship in real time. In particular, the use of the lateral G sensor signal makes it possible to identify the cornering stiffness and vehicle sideslip angle without any a priori knowledge on the road bank angle. The performance of the proposed algorithms is verified through simulation and experimental results.     

Evaluation of a sliding mode observer for vehicle sideslip angle

This paper presents a sliding mode observer of vehicle sideslip angle, which is the principal variable relating to the transversal forces at the tire/road interface. The vehicle is first modelled, and the model is subsequently simplified. This study validates the observer using both a validated simulator and real experimental data acquired by the Heudiasyc laboratory car, and also shows the limitations of this method. The observer requires a yaw rate sensor and data about vehicle speed are required in order to estimate sideslip angle. Some properties of the nonlinear observability matrix condition number are discussed, and relations between this variable and observation error, vehicle speed and tire cornering stiffness are presented.     

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．     

轮式移动机器人滑移量重建与滑移补偿控制

为解决轮式移动机器人的滑移补偿控制问题,首先推导出车体侧滑角的表达式,然后将时变侧滑角的重建问题转化为对地面特性参数的辨识问题.利用Luenberger观测器设计出自适应辨识律,并证明了当控制输入满足持续激励条件时,可以准确辨识出地面特性参数.基于链式系统模型设计出滑移补偿控制器,在滑移角精确已知的条件下,可以保证位置误差收敛,姿态误差有界.仿真结果表明,基于所设计的自适应辨识律,可以准确地重建出滑移角,提高滑移控制精度.     

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。     

Disturbance Observer Based Control for Four Wheel Steering Vehicles With Model Reference

This paper presents a disturbance observer based control strategy for four wheel steering systems in order to improve vehicle handling stability. By combination of feedforward control and feedback control, the front and rear wheel steering angles are controlled simultaneously to follow both the desired sideslip angle and the yaw rate of the reference vehicle model. A nonlinear three degree-of-freedom four wheel steering vehicle model containing lateral, yaw and roll motions is built up, which also takes the dynamic effects of crosswind into consideration. The disturbance observer based control method is provided to cope with ignored nonlinear dynamics and to handle exogenous disturbances. Finally, a simulation experiment is carried out, which shows that the proposed four wheel steering vehicle can guarantee handling stability and present strong robustness against external disturbances.      

Vehicle sideslip estimator using load sensing bearings

This paper investigates the potential of load based vehicle sideslip estimation. Different techniques to measure tyre forces have been presented over the years; so far no technique has made it to the market. This paper considers a new technology based on load sensing bearings, which provides tyre force measurements. Based on the features of the sensor, a vehicle sideslip angle estimator is designed, analyzed and tested. The paper shows that direct tyre force sensing has mainly two advantages over traditional model-based estimators: primarily, it avoids the use of tyre models, which are heavily affected by uncertainties and modeling errors and secondarily, providing measurements on the road plane, it is less prone to errors introduced by roll and pitch dynamics. Extensive simulation tests along with a detailed analysis of experimental tests performed on an instrumented vehicle prove that the load based estimation outperforms the kinematic model-based benchmark yielding a root mean square error of 0.15°.     

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.     

Application of global sensitivity analysis to a tire model with correlated inputs

When a vehicle equipped with tire is manoeuvred on the ground, the tires are submitted to a number of forces – longitudinal force when driving or braking torque is applied to the wheel and/or lateral force when the wheel is steered to turn at a corner. Pacejka model describes these forces that represent the reaction of the road onto the tire. This nonlinear model depends on correlated parameters such as the friction coefficient, the vertical load, and the cornering stiffness, which have to be identified from some measurements. The sensitivity of Pacejka model to these correlated parameters are studied using an approach based on polynomial chaos. It consists in decorrelating the parameters using the Nataf transformation and then, in expanding the model output onto polynomial chaos. The sensitivity indices are then obtained straightforwardly from the algebraic expression of the coefficients of the polynomial expansion.     

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.     

非独立悬架车辆自激摆振的数值仿真分析

基于一个三自由度的转向系统模型,利用数值仿真方法分析了横拉杆刚度、主销后倾角、转向机刚度、轮胎侧偏刚度、轮胎拖距、转向机阻尼、绕主销当量阻尼等参数对载重汽车自激型摆振的影响.仿真分析的结果表明,上述参数发生变化时,可诱发自激摆振,但车速也是影响摆振的关键因素之一.在确定的系统参数和车速下,初始激励不仅可能诱发稳定的自激摆振,还可能是发散的运动.与受迫型自激摆振不同,自激型摆振的频率变化与车速的变化并不一致.     

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.

     

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.     

Estimations of previewed road curvatures and vehicular motion by a vision-based data fusion scheme

In this study, a new framework of vision-based estimation is developed using some data fusion schemes to obtain previewed road curvatures and vehicular motion states based on the scene viewed from an in-vehicle camera. The previewed curvatures are necessary for the guidance of an automatically steering vehicle, and the desired vehicular motion variables, including lateral deviation, heading angle, yaw rate, and sideslip angle, are also required for proper control of the vehicular lateral motion via steering. In this framework, physical relationships of previewed curvatures among consecutive images, motion variables in terms of image features searched at various levels in the image plane, and dynamic correlation among vehicular motion variables are derived as bases of data fusion to enhance the accuracy of estimation. The vision-based measurement errors are analyzed to determine the fusion gains based on the technique of a Kalman filter such that the measurements from the image plane and predictions of physical models can be properly integrated to obtain reliable estimations. Off-line experimental works using real road scenes are performed to verify the whole framework for image sensing.     

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.     

A novel integrated control framework of AFS,ASS, and DYC based on ideal roll angle to improve vehicle stability

The current research on vehicle stability control mainly focuses on following the ideal yaw rate and sideslip angle, without considering the potential of ideal roll angle in improving the vehicle stability. In addition, the mutation of tire-road friction coefficient promotes a great challenge to the stability control. To improve the vehicle stability, in this study, firstly, the three-dimensional stability region of “lateral speed-yaw rate-roll angle” was studied, and a method to determine the ideal roll angle was proposed. Secondly, a novel integrated control framework of AFS, ASS, and DYC based on ideal roll angle was proposed to actively control the front tire slip angles, suspension forces, and motor torques: In the upper-level controller, model predictive control and tire force distribution algorithm were used to obtain the optimal four-tire longitudinal forces, front tire lateral forces and additional roll moment under constraints; In the lower-level controller, the upper virtual target was realized by the optimal allocation algorithm of actuators and the tire slip controller. Finally, the proposed control framework was verified on the varied-µ road. The results indicated that compared with the two existing control strategies, the proposed framework can significantly improve the vehicle following performance and stability.     

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．     

自动驾驶4WS车辆路径跟踪最优控制算法仿真

针对自动驾驶车辆高速主动转向工况下传统的控制算法的控制效果容易出现较多的超调量和较长调节时间的问题,提出了基于车辆动力学模型的轨迹预测跟踪主动转向控制算法,并基于轮胎侧偏刚度非线性的特性设计了权系数线性最优二次型(LQR)后轮转角控制算法,通过联合仿真对控制算法效果进行了验证。仿真结果表明:自动驾驶四轮转向车辆在低、高速工况下进行自主换道行驶时,算法控制效果满足汽车操纵稳定性要求,且权系数LQR后轮转向算法比定侧偏刚度的LQR线性控制算法有更优越的操控性能。     

Joint ego-motion and road geometry estimation

We provide a sensor fusion framework for solving the problem of joint ego-motion and road geometry estimation. More specifically we employ a sensor fusion framework to make systematic use of the measurements from a forward looking radar and camera, steering wheel angle sensor, wheel speed sensors and inertial sensors to compute good estimates of the road geometry and the motion of the ego vehicle on this road. In order to solve this problem we derive dynamical models for the ego vehicle, the road and the leading vehicles. The main difference to existing approaches is that we make use of a new dynamic model for the road. An extended Kalman filter is used to fuse data and to filter measurements from the camera in order to improve the road geometry estimate. The proposed solution has been tested and compared to existing algorithms for this problem, using measurements from authentic traffic environments on public roads in Sweden. The results clearly indicate that the proposed method provides better estimates.