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.     

汽车行驶稳定性动力学优化控制仿真研究

汽车行驶稳定动力学优化控制,旨在通过确定汽车行驶状态下纵向车速、横摆角速度、质心侧偏角等重要的状态变量,提前预知汽车未来时刻的可能的行驶状态,并将其输送到汽车底盘主动控制系统,实现动力学优化控制,提高汽车的主动安全性,减少道路交通事故.寻找一种低成本、高精度且能够实时获得车辆重要状态参数的方法,是汽车稳定行驶动力学优化控制的关键技术之一.利用Madab/Simulink仿真工具,分别建立了汽车动力学仿真模型和车辆行驶状态Kalman滤波估计仿真模型,可以同时实现对车辆行驶状态的仿真和对车辆行驶过程中横摆角速度、侧向加速度和质心侧偏角的估计,并且模型具有可扩展性.最后进行了实车场地试验,完成了阶跃曲线、双移线等操作,通过模型仿真、试验数据和状态估计结果的比较得出,三者一致性较好,同时验证了车辆动力学仿真模型和状态估计算法仿真模型的有效性和通用性.     

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.     

多工况适应的车辆质心侧偏角观测算法研究

研究汽车高速运行稳定性优化控制问题,在车辆稳定性控制中,质心侧偏角是衡量稳定性的重要指标,观测对于稳定性控制非常重要。针对目前车载多传感器信息的观测条件,为解决质心侧偏角观测的准确性、快速性和多工况适应性问题,提出了一种融合卡尔曼滤波和信号积分的质心侧偏角观测算法。观测算法充分考虑了车辆动力学特性,采用车辆运行过程的多种工况进行了算法设计及切换。最后在Matlab/Simulink平台上搭建了质心侧偏角观测仿真实验平台,通过多工况下的仿真,对所提出的质心侧偏角观测算法进行了仿真验证,结果表明能快速准确地矫正质心侧偏角,使稳态误差减小。     

惯性信息状态观测器在车辆稳定系统中的应用

针对车辆安全稳定系统的算法设计中诸如车身质心侧偏角等惯性信息难以估量的问题,研究了状态观测器在系统中的应用方法。建立了车辆二自由度单车道数学模型,设计了质心侧偏角状态观测器的流程,并对此流程中的估算条件和影响因素进行了计算分析。通过数值计算类软件Matlab和动力学仿真类软件Adams的联合仿真,验证了所设计的状态观测器的精度能够满足车辆安全稳定系统的算法设计中的要求。     

汽车电子稳定程序模糊控制仿真

关于汽车安全和稳定性问题,针对汽车电子稳定程序(Electronic stability program,ESP)是一种主动安全系统.为了提高汽车在转向工况下的侧向稳定性,采用快速控制原型的方法,建立了基于H.B.Pacejka 轮胎模型的三自由度整车模型和车辆参考模型.选取车辆横摆角速度和质心侧偏角作为综合控制变量,设计了模糊控制器,并对某款车型进行了离线仿真和在线实时仿真.结果表明,设计的控制器可以很好的控制汽车的横摆角速度和质心侧偏角.改进后的方法可以提高控制系统的实时性和稳定性,为ESP的快速开发提供了参考依据.     

基于模糊控制的汽车直接横摆力矩研究

为防止汽车产生测滑,针对汽车直接横摆力矩控制,提出了横摆角速度与质心侧偏角联合控制的模糊控制方法.横摆力矩控制采用分层控制方法,设计了模糊控制器和规则制动力分配方法.模糊控制器根据期望值和车辆状态决策出所需的附加横摆力矩,并通过规则制动力分配方法进行主动差动制动实现.采用Matlab/Simulink与CarSim联合仿真对控制方法进行了仿真验证.结果表明:横摆角速度与质心侧偏角联合控制的横摆力矩模糊控制方法使汽车能够较好地跟踪期望,有效提高汽车极限工况下的行驶稳定性.     

基于模糊控制的分布式电动车辆横摆力矩研究

为提高多轮分布式电驱动车辆在不同工况下的操纵稳定性,设计了一种基于直接横摆力矩控制的分层控制策略。上层以横摆角速度和质心侧偏角为控制变量,采用模糊控制进行目标运动状态跟踪,决策出所需要的横摆力矩。下层按设计的规则进行转矩分配。应用TruckSim和Matlab/Simulink建立车辆和控制器模型,分别在高、低附着等工况下进行联合仿真。仿真结果表明,设计的模糊控制方法能对车辆目标状态进行良好跟踪,相较于无控制状态能够提高车辆的操纵稳定性。     

轮毂驱动电动车辆动力学稳定性滑模控制策略研究

为了提高轮毂驱动电动车辆的操纵稳定性,理论分析了车辆横摆角速度和质心侧偏角对于车辆稳定性的影响.设计了基于滑模变结构控制理论和直接横摆力矩控制的双层控制器.在Carsim中搭建了四轮轮毂电机驱动车辆仿真实验平台,并进行了Carsim/Simulink联合仿真,在标准换道工况下,分别验证了基于质心侧偏角的滑模变结构控制和基于横摆角速度的滑模变结构控制策略的效果,验证了双层车辆稳定性控制策略的有效性和稳定性.     

车辆主动转向和独立驱动集成控制

针对车辆行驶安全的主动控制问题,本文提出一种主动转向和4轮独立驱动的双层集成控制系统.针对所建立的8自由度汽车模型,利用上层控制结构的质心侧偏角—质心侧偏角速度β–■相平面算法,实现对车辆运动状态的确定;利用下层控制结构的滑模变结构和二次规划算法,实现主动转向和驱动力的协调,控制车辆的运行状态.在MATLAB/Simulink下,对集成控制器进行增幅正弦极限工况的仿真,结果显示该集成控制器可将车辆的横摆和侧滑控制在安全的区域内,明显提高极限工况下的车辆稳定性.     

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.     

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.     

Modeling and simulation of overtaking behavior involving environment

Overtaking is a complex driving behavior for intelligent vehicles. Current research on modeling overtaking behavior pays little attention on the effect of environment. This paper focuses on the modeling and simulation of the overtaking behavior in virtual reality traffic simulation system involving environment information, such as road geometry and wind. First, an intelligent vehicle model is proposed to better understand environment information and traffic situation. Then, overtaking behavior model is introduced in detail, the lane changing feasibility is analyzed and the fuzzy vehicle controllers considering the road and wind effect are researched. Virtual reality traffic simulation system is designed to realize the simulation of overtaking behavior, with realistic road geometry features. Finally, simulation results show the correctness and the effectiveness of our approach.     

Sliding‐mode control for automated lane guidance of heavy vehicle

In the present work, an active steering assistance system for heavy vehicles is developed to prevent lane departure and let the vehicle follow the road's centre line. The controller is based on the super‐twisting algorithm. An estimator based on the sliding mode observer is also developed to estimate the vehicle dynamics. The lateral positions and speeds are then controlled to keep the vehicle in the centre of the lane. The lateral offset and the relative yaw angle are supposed to be measured and the road curvature to be known. Some simulation results are given to show the quality of this steering assistance system. Copyright © 2011 John Wiley & Sons, Ltd.     

基于多项式的智能车辆换道轨迹规划

以智能车辆换道过程为研究对象,提出一种基于多项式理论的车辆换道轨迹规划算法。该算法采用矩形对换道车辆及障碍车辆进行包裹,结合换道车辆的边界条件由以时间为参数的多项式计算得到换道轨迹。由该算法生成的换道轨迹符合四段式车道变换模型,并适用于复杂道路环境。新算法将复杂道路环境中期望换道轨迹的求取问题转换为单一参数求取问题,简化了计算,同时考虑了车辆动力学限制对生成轨迹的影响。计算机仿真验证了算法的正确性及有效性,尤其是在复杂路面情况下体现了该换道轨迹规划算法的优势。     

Integrated stability control of AFS and DYC for electric vehicle based on non-smooth control

A controller which ensures the driving stability of a four-wheel-independent-drive electric vehicle (4WID-EV) is designed in this paper. The controller is structurally hierarchically designed. In order to keep the 4WID-EV running steadily, an upper-level controller integrating the active front-wheel steering control method (AFS) and direct yaw moment control method (DYC) is designed to keep the sideslip angle and yaw rate tracking the ideal values. A non-smooth control method is used to improve the closed-loop system's convergence and anti-disturbance performance. The additional yaw moment generated by the upper-level controller is distributed to four driving wheels by the lower-level controller. An optimal control algorithm is used in the lower-level controller to achieve the minimum sum of tire utilisation, and ensure the power performance and driving stability of the 4WID-EV. In order to verify the effectiveness of the designed controller, a simulation model of the stability control system is established based on Carsim-Matlab/Simulink. And the simulation is performed under double lane change road considering the disturbances. The results of the simulation show that the 4WID-EV with the designed controller achieves smaller sideslip angle than sliding-mode control and the actuator chatter is slight. Then the stability and safety of the 4WID-EV are greatly improved.     

结合状态机和动态目标路径的无人驾驶决策仿真

目的 决策系统是无人驾驶技术的核心研究之一。已有决策系统存在逻辑不合理、计算效率低、应用场景局限等问题,因此提出一种动态环境下无人驾驶路径决策仿真。方法 首先,基于规则模型构建适于无人驾驶决策系统的交通有限状态机;其次,针对交通动态特征,提出基于统计模型的动态目标路径算法计算状态迁移风险;最后,将交通状态机和动态目标路径算法有机结合,设计出一种基于有限状态机的无人驾驶动态目标路径模型,适用于交叉口冲突避免和三车道换道行为。将全速度差连续跟驰模型运用到换道规则中,并基于冲突时间提出动态临界跟车距离。结果 为验证模型的有效性和高效性,对交通环境进行虚拟现实建模,模拟交叉口通行和三车道换道行为,分析文中模型对车流量和换道率的影响。实验结果显示,在交叉口通行时,自主车辆不仅可以检测冲突还可以根据风险评估结果执行安全合理的决策。三车道换道时,自主车辆既可以实现紧急让道,也可以通过执行换道达成自身驾驶期望。通过将实测数据和其他两种方法对比,当车流密度在0.20.5时,本文模型的平均速度最高分别提高32 km/h和22 km/h。当车流密度不超过0.65时,本文模型的换道成功率最高分别提升37%和25%。结论 实验结果说明本文方法不仅可以在动态城区环境下提高决策安全性和正确性,还可以提高车流量饱和度,缓解交通堵塞。     

基于微分平坦与MPC的智能车换道控制算法

为了提高智能车换道的安全性,提出了一种基于微分平坦理论与模型预测控制(MPC)算法相结合的智能车换道轨迹规划与跟踪算法。该算法利用约束求解得到基于sigmoid函数的优化路径;将其与多项式参数化时间函数作为平坦输出,利用微分平坦理论构造一个非线性性能指标函数并对其进行优化求解完成车速规划;从而实现对智能车辆路径-速度分解式的轨迹规划。利用动力学模型预测控制算法线上控制的优点,对智能车的车轮转向进行实时控制,使得车辆按照规划好的轨迹行驶完成换道。通过CarSim与MATLAB/Simulink的联合仿真,将提出的轨迹规划算法应用于车辆系统仿真软件中进行验证,结果表明该算法能够实现对智能车进行轨迹规划和跟踪控制,使其安全高效地换至目标车道。     

Estimation of Vehicle Pose and Position with Monocular Camera at Urban Road Intersections

With the rapid development of urban, the scale of the city is expanding day by day. The road environment is becoming more and more complicated. The vehicle ego-localization in complex road environment puts forward imperative requirements for intelligent driving technology. The reliable vehicle ego-localization, including the lane recognition and the vehicle position and attitude estimation, at the complex traffic intersection is significant for the intelligent driving of the vehicle. In this article, we focus on the complex road environment of the city, and propose a pose and position estimation method based on the road sign using only a monocular camera and a common GPS (global positioning system). Associated with the multi-sensor cascade system, this method can be a stable and reliable alternative when the precision of multi-sensor cascade system decreases. The experimental results show that, within 100 meters distance to the road signs, the pose error is less than 2 degrees, and the position error is less than one meter, which can reach the lane-level positioning accuracy. Through the comparison with the Beidou high-precision positioning system L202, our method is more accurate for detecting which lane the vehicle is driving on.