基于IEKF-APF算法的汽车状态估计

行驶汽车状态变量质心侧偏角和横摆角速度是汽车稳定性控制系统中重要控制变量,准确获取行驶过程中的状态信息是汽车控制系统研究的关键问题。应用估计理论由传感器测出易测变量来估计难以测量的关键状态变量是一种常用的估计方法。提出一种新的粒子滤波算法通过所建立的包含定常平稳随机噪声和非线性轮胎的汽车动力学7自由度整车模型对汽车状态进行估计。针对粒子滤波过程中出现的退化问题,应用迭代扩展卡尔曼滤波融入最新观测信息产生更加接近真实状态的重要性密度函数,辅助粒子滤波算法通过所产生的重要性密度函数结合观测量进行重采样,结合这两种算法提出迭代扩展卡尔曼-辅助粒子滤波算法(Iterative extended Kalman filtering-auxiliary particle filtering algorithm, IEKF-APF)以改善粒子采样和估计精度的提高。为验证所提出的IEKF-APF算法估计性能,将其结果与实车试验结果和无迹卡尔曼滤波算法(Unscented Kalman filtering, UKF)估计结果进行比较,结果表明其估计性能优于UKF,更接近于试验结果。     

基于双重扩展自适应卡尔曼滤波的汽车状态和参数估计

准确实时地获取行驶过程中的状态信息是汽车动态控制系统研究的关键,为此提出了一种新的汽车状态估计器。建立了包含不准确模型参数和未知时变统计特性噪声的非线性汽车动力学模型,针对该非线性系统提出一种双重扩展自适应卡尔曼滤波算法（DEAKF）。该算法采用两个卡尔曼滤波器并行运算,状态估计和参数估计互相更新,同时将带遗忘因子的噪声统计估值器嵌入到状态校正过程和参数校正过程之间,以解决系统的噪声时变问题。基于ADAMS的虚拟试验和实车试验结果表明,该算法的状态估计精度高于EKF方法和DEKF方法的状态估计精度,同时具有良好的模型参数校正能力,对汽车动态控制系统中估计器的设计具有理论指导意义。
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基于蚁群优化UKF算法的汽车状态估计

针对汽车状态估计中过程噪声和观测噪声的时变特性,提出一种新的自适应滤波算法。该算法基于三自由度非线性汽车动力学模型,在利用UKF对汽车状态量进行估计的同时,引入蚁群优化算法,根据目标函数对过程噪声和观测噪声进行寻优,实现了过程噪声和观测噪声的自适应作用,估计精度的大幅提高。虚拟实验验证了蚁群优化UKF算法的鲁棒性和精度。研究结果对汽车主动控制系统的开发具有重大的理论指导意义。     

重力/地磁辅助的欧拉角无迹卡尔曼滤波姿态估计

为了克服应用扩展卡尔曼滤波(EKF)的姿态估计算法的线性化误差问题,提出了一种基于重力/地磁辅助的欧拉角无迹卡尔曼滤波(UKF)姿态估计算法来提升低成本微机电系统(MEMS)的姿态测量精度。应用重力与地磁数据抑制了MEMS姿态误差快速发散问题;将欧拉角作为状态,应用四元数完成时间更新过程中的姿态更新,避免了四元数作为状态的规范化问题及欧拉角姿态更新精度低的问题;由于UKF滤波器不存在线性化误差,故其具有更好的稳定性和姿态估计精度。应用实际MEMS数据开展的算法验证实验显示:与EKF姿态估计算法相比,提出的UKF姿态估计算法得到的俯仰与横滚角精度提高了近20%,航向角精度提高了12.1%。结果表明:本文提出算法的精度更高;然而由于UKF算法对状态协方差估计不足,其收敛时间有所增加。     

基于MIT规则的自适应Unscented卡尔曼滤波及其在旋翼飞行机器人容错控制的应用

提出一种新颖的基于MIT规则的自适应Unscented卡尔曼滤波(Unscented Kalman filter,UKF)算法,用来进行参数以及状态的联合估计.针对旋翼飞行机器人执行器提出一种执行器健康因子(Actuator health coefficients,AHCs)的故障模型结构,应用自适应UKF对AHCs参数进行在线估计,将联合估计的状态以及故障参数引入基于模型的反馈线性化控制结构,组成完整的容错控制系统.提出的自适应UKF算法以及容错控制结构经过中科院沈阳自动化研究所ServoHeli-20旋翼无人智能平台数学模型进行仿真试验验证,效果良好.     

径向基粒子滤波状态跟踪测量算法

状态跟踪测量的过程噪声降低了目标信噪比,增加了自适应滤波跟踪的难度。当误差较大时,基本粒子滤波算法的预测累积误差效应将导致系统发散。无迹粒子滤波算法利用无迹卡尔曼滤波提高重要性函数估计精度,减少后验概率密度分布误差,但同时也将大幅增加运算时间。提出一种基于径向基函数网络(RBFN)的改进型粒子滤波算法PF-RBF,利用RBFN通过目标状态观测值和全局预测值拟合状态变化趋势,更新各粒子状态,提高先验概率密度分布估计精度,消除过程噪声引起的估计误差。与无迹粒子滤波(UPF)算法相比,该算法无需构造无迹卡尔曼滤波(UKF)重要性函数,提高了运算速度。机动目标跟踪试验表明,径向基粒子滤波算法在线性和非线性观测方程下的状态跟踪测量精度和算法稳定性均优于UKF、PF和UPF算法,可有效实现对状态变化的实时鲁棒跟踪。当参与运算的粒子数增加时,PF-RBF算法执行时间的增长速率较UPF算法更低,可满足高精度状态跟踪应用。     

汽车S-AKF估计与PID控制的闭环系统仿真

基于Carsim和Simulink设计了基于PID的横摆角速度控制策略,并对控制策略的效果进行了验证;建立了二自由度汽车动力学模型,提出将S-自适应卡尔曼滤波（S-AKF）算法用于汽车横摆角速度和质心侧偏角的估计;将S-AKF估计器与PID控制器相结合,构成完整的估计-控制闭环系统。通过Carsim中的操稳试验对整个系统的性能进行了仿真验证,结果显示,提出的估计-控制体系可在必要工况下实现对汽车的主动干预,提高汽车的路径跟踪能力和稳定性能。     

汽车动力学参数的相对灵敏度分析

基于灵敏度的函数求导思想,变换出单因素扰动法。用该方法首次对基于卡尔曼滤波理论进行状态估计后的汽车状态参数的相对敏感度进行分析。在线性二自由度汽车模型和卡尔曼滤波理论基础上,分析估计所得的状态量(横摆角速度、质心侧偏角)对动力学模型中的汽车的质量、转动惯量以及质心到前轴距离的灵敏度。研究汽车状态参数对汽车结构参数的相对灵敏度,通过仿真试验和实车试验数据对相对灵敏度进行分析,最终的结论将对汽车状态估计算法的研究具有指导意义。     

UKF在永磁直线同步电动机无位置传感器控制中的应用

Unscented卡尔曼滤波器(UKF)在许多非线性估计问题中是一种估计性能优于扩展卡尔曼滤波器(EKF)的非线性滤波方法。然而在永磁直线同步电动机无位置传感器控制中,UKF是否能提高永磁直线电动机位置与速度的估计性能却尚未见研究。针对永磁直线同步电动机进给系统的特点,建立用于位置与速度估计器的永磁直线同步电动机进给系统模型,提出永磁直线同步电动机无位置传感器控制系统。通过仿真和试验对UKF的估计性能进行评估,并与EKF进行了比较。仿真及试验结果表明,UKF在估计性能与EKF相当, 然而UKF的计算量却比EKF大,使得在高速永磁直线同步电动机无位置传感器控制这一特定问题上,EKF比UKF更有效。     

基于AUKF算法的锂电池SOC估算

针对无迹卡尔曼滤波(UKF)算法在估算锂电池荷电状态(SOC)时忽略了系统噪声时变特性问题,导致估算精度降低,故提出了自适应无迹卡尔曼滤波(AUKF)算法.通过建立电池模型,利用UKF对SOC进行估算的同时,结合Sage-Husa自适应滤波算法时刻对系统噪声进行估计和修正,进而提高估算SOC时的精度.通过在不同工况下对...     

A novel technique for optimal integration of active steering and differential braking with estimation to improve vehicle directional stability

This study deals with the enhancement of directional stability of vehicle which turns with high speeds on various road conditions using integrated active steering and differential braking systems. In this respect, the minimum usage of intentional asymmetric braking force to compensate the drawbacks of active steering control with small reduction of vehicle longitudinal speed is desired. To this aim, a new optimal multivariable controller is analytically developed for integrated steering and braking systems based on the prediction of vehicle nonlinear responses. A fuzzy programming extracted from the nonlinear phase plane analysis is also used for managing the two control inputs in various driving conditions. With the proposed fuzzy programming, the weight factors of the control inputs are automatically tuned and softly changed. In order to simulate a real-world control system, some required information about the system states and parameters which cannot be directly measured, are estimated using the Unscented Kalman Filter (UKF). Finally, simulations studies are carried out using a validated vehicle model to show the effectiveness of the proposed integrated control system in the presence of model uncertainties and estimation errors.     

Dual extended Kalman filter for combined estimation of vehicle state and road friction

Vehicle state and tire-road adhesion are of great use and importance to vehicle active safety control systems. However, it is always not easy to obtain the information with high accuracy and low expense. Recently, many estimation methods have been put forward to solve such problems, in which Kalman filter becomes one of the most popular techniques. Nevertheless, the use of complicated model always leads to poor real-time estimation while the role of road friction coefficient is often ignored. For the purpose of enhancing the real time performance of the algorithm and pursuing precise estimation of vehicle states, a model-based estimator is proposed to conduct combined estimation of vehicle states and road friction coefficients. The estimator is designed based on a three-DOF vehicle model coupled with the Highway Safety Research Institute(HSRI) tire model; the dual extended Kalman filter (DEKF) technique is employed, which can be regarded as two extended Kalman filters operating and communicating simultaneously. Effectiveness of the estimation is firstly examined by comparing the outputs of the estimator with the responses of the vehicle model in CarSim under three typical road adhesion conditions(high-friction, low-friction, and joint-friction). On this basis, driving simulator experiments are carried out to further investigate the practical application of the estimator. Numerical results from CarSim and driving simulator both demonstrate that the estimator designed is capable of estimating the vehicle states and road friction coefficient with reasonable accuracy. The DEKF-based estimator proposed provides the essential information for the vehicle active control system with low expense and decent precision, and offers the possibility of real car application in future.     

基于状态观测器的4WS车辆最优随动操纵研究

建立了包含车身侧倾特性、转向系统特性和制动特性的四轮转向车辆动力学随动操纵控制模型。考虑到作为车辆状态量之一的车辆质心侧偏角难以测量,设计了用于重构车辆状态的状态观测器。最后,基于重构的车辆状态,运用最优控制理论设计了四轮转向车辆随动操纵控制器,实现了所谓的线传操纵(Steer by wire)。仿真表明,反映车辆操纵性能的车辆状态量能很好地跟踪驾驶员发出的操纵指令,车辆具有独特、良好的机动性能。     

一种快速GPS/DR组合导航系统状态估计算法研究

无轨迹卡尔曼滤波(UKF)技术在非线性系统(GPS/DR车载组合导航系统)的状态估计中取得了比扩展卡尔曼滤波(EKF)更好的滤波精度和收敛速度.为了进一步减少采样点数目,提高UKF滤波实时性,一组n+2个采样点被构造用于逼近系统状态分布.蒙特卡洛仿真表明RUKF和UKF在滤波精度和收敛速度上是一致的,RUKF的计算效率好于UKF.     

四轮独立驱动汽车多工况路面附着系数识别研究

四轮独立驱动（Four-wheel independent drive, 4WID）电动汽车具有较好的动力学稳定性协调控制潜力,轮胎-路面附着系数的识别是汽车操纵性能控制的基础。基于视觉传感器的路面识别方案成本高,传统卡尔曼滤波算法观测精度低且难以适应含有时变结构的非线性系统。众多研究已详细介绍均一路面附着系数的估计方法,尚未充分考虑对接和对开路面的观测方案。本研究将强跟踪理论（Strong tracking theory, STT）引入无迹卡尔曼滤波（Unscented Kalman filter, UKF）算法,构造强跟踪无迹卡尔曼滤波（Strong tracking unscented Kalman filter, STUKF）观测器,提高识别算法的识别精度,以及对时变附着系数的适应能力。考虑多重渐消因子计算过程的复杂性,本研究降低四轮路面识别模型的维数,构造两个含单重渐消因子矩阵的二维观测器,实时观测四个轮胎-路面附着系数。仿真和试验结果表明,与传统四维UKF算法相比,改进的并联STUKF算法能够更加有效地追踪转向或直行工况下均一路面、对接路面和对开路面的四轮附着系数。     

Kinematic modeling of a track geometry using an Unscented Kalman Filter

New data filtering methods based on the Kalman filter concept are investigated for application to track geometry, where a track trolley is normally used to measure the track coordinates, cant and gauge, among others. Continuous as well as discrete measurements of the trolley are conceptually modeled, and the accuracy of the sampling scheme is also investigated. For this, a modified kinematic model of the track geometry involving transition and circular curves is proposed based on the tangential as well as normal acceleration, and both a nonlinear Extended Kalman Filter (EKF) and an augmented Unscented Kalman Filter (UKF) are applied to optimize the measured data. The efficiency and accuracy of the proposed models of EKF and UKF having a new kinematic equation is verified with ideal track geometry and with statistical methods including Root Mean Square (RMS). In addition, field measurement data are also considered to check the applicability of the models. Finally, future work on track geometry modeling based on a high-speed measurement vehicle is briefly outlined.     

基于状态空间自回归最小二乘的车辆质心侧偏角非线性估计

基于动力学模型对车辆质心侧偏角进行了估计。为使轮胎模型能够适应不同附着系数的路面,将动态参数引入“魔术公式”轮胎侧偏力模型。应用状态空间形式的自回归最小二乘算法（RLS）设计了车辆质心侧偏角估计器。通过车辆在高附着系数路面的蛇形试验和变附着系数路面的双移线试验对估计方法进行了验证,结果表明,即使车辆出现大侧偏情况使轮胎进入到侧偏角-侧偏力特性曲线的非线性区域,提出的估计方法也能够实现对质心侧偏角的估计。将该估计方法与扩展卡尔曼滤波估计在精度、计算效率和使用条件等方面进行了比较,进一步表明所提出方法具有良好性能。