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.     

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.     

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.     

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.     

A control matching model predictive control approach to string stable vehicle platooning

A predictive control strategy for vehicle platoons is presented in this paper, accommodating both string stability and constraints (e.g., physical and safety) satisfaction. In the proposed design procedure, the two objectives are achieved by matching a model predictive controller (MPC), enforcing constraints satisfaction, with a linear controller designed to guarantee string stability. The proposed approach neatly combines the straightforward design of a string stable controller in the frequency domain, where a considerable number of approaches have been proposed in literature, with the capability of an MPC-based controller enforcing state and input constraints.A controller obtained with the proposed design procedure is validated both in simulations and in the field test, showing how string stability and constraints satisfaction can be simultaneously achieved with a single controller. The operating region that the MPC controller is string stable is characterized by the interior of feasible set of the MPC controller.     

Dynamic path tracking control of a vehicle on slippery terrain

This paper deals with accuracy and reliability for the path tracking control of a four wheel mobile robot with a double-steering system when moving at high dynamics on a slippery surface. An extended kinematic model of the robot is developed considering the effects of wheel–ground skidding. This bicycle type model is augmented to form a dynamic model that considers an actuation of the four wheels. Based on the extended kinematic model, an adaptive and predictive controller for the path tracking is developed to drive the wheels front and rear steering angles. The resulting control law is combined with a stabilization algorithm of the yaw motion which modulates the actuation torque of each four wheels, on the basis of the robot dynamic model. The global control architecture is experimentally evaluated on a wet grass slippery terrain, with speeds up to 7 m/s. Experimental results demonstrate enhancement of tracking performances in terms of stability and accuracy relative to the kinematic control.     

Two‐stage 3D model‐based UAV pose estimation: A comparison of methods for optimization

Particle Filters (PFs) have been successfully used in three‐dimensional (3D) model‐based pose estimation. Typically, these filters depend on the computation of importance weights that use similarity metrics as a proxy to approximate the likelihood function. In this paper, we explore the use of a two‐stage 3D model‐based approach based on a PF for single‐frame pose estimation. First, we use a classifier trained in a synthetic data set for Unmanned Aerial Vehicle (UAV) detection and a pretrained database indexed by bounding boxes properties to obtain an initial rough pose estimate. Second, we employ optimization algorithms to optimize the used similarity metrics and decrease the obtained error. We have tested four different algorithms: (a) Particle Filter Optimization (PFO), (b) Particle Swarm Optimization (PSO), (c) modified PSO, and (d) an approach based on the evolution strategies present in the genetic algorithms named Genetic Algorithm‐based Framework (GAbF). To check the quality of the estimate on each iteration, we have tested several similarity metrics (color, edge, and mask‐based) based on the UAV Computer‐Aided Design (CAD) model. The framework is applied to the outdoor pose estimation of a fixed‐wing UAV for autonomous landing in a Fast Patrol Boat (FPB). We extend our previous approach by adopting a better problem formulation, using Deep Neural Networks (DNNs) for UAV detection, making the comparison between the used similarity metrics, comparing pose optimization schemes, and showing new results. The future work will focus on the inclusion of this scheme in a tracking architecture to increase the accuracy of the result between observations.     

A discrete-time parameter estimation based adaptive actuator failure compensation control scheme

This article studies discrete-time adaptive failure compensation control of systems with uncertain actuator failures, using an indirect adaptive control method. A discrete-time model of a continuous-time linear system with actuator failures is derived and its key features are clarified. A new discrete-time adaptive actuator failure compensation control scheme is developed, which consists of a total parametrisation of the system with parameter and failure uncertainties, a stable adaptive parameter estimation algorithm, and an on-line design procedure for feedback control. This work provides a new design of direct adaptive compensation of uncertain actuator failures, using an indirect adaptive control method. Such an adaptive design ensures desired closed-loop system stability and tracking properties despite uncertain actuator failures. Simulation results are presented to show the desired adaptive actuator failure compensation performance.     

Launch control for sport motorcycles: A clutch-based approach

Launch control is an innovative active system which offers an appealing automatic feature for sport motorcycles. Due to the high power of the engine and to the complexity of the torque transfer dynamics driving a smooth and safe start from a standstill might be difficult for non-expert riders in some working conditions. As such, an active controller capable of safely managing the vehicle acceleration from a standing start might effectively support the rider as far as both safety and performance are concerned. To control such a maneuver, two possible actuators can be employed: the engine throttle and the clutch. This paper illustrates the analysis and the design of a clutch-based launch controller for sport motorcycles, demonstrating that it allows the accurate tracking of an acceleration set-point thanks to a dedicated clutch position controller. Experimental results are presented that demonstrate the effectiveness of the proposed approach.     

Robust moving horizon estimation based output feedback economic model predictive control

In this work, we develop an economic model predictive control scheme for a class of nonlinear systems with bounded process and measurement noise. In order to achieve fast convergence of the state estimates to the actual system state as well as the robustness of the observer to measurement and process noise, a deterministic (high-gain) observer is first applied for a small time period with continuous output measurements to drive the estimation error to a small value; after this initial small time period, a robust moving horizon estimation scheme is used on-line to provide more accurate and smoother state estimates. In the design of the robust moving horizon estimation scheme, the deterministic observer is used to calculate reference estimates and confidence regions that contain the actual system state. Within the confidence regions, the moving horizon estimation scheme is allowed to optimize its estimates. The output feedback economic model predictive controller is designed via Lyapunov techniques based on state estimates provided by the deterministic observer and the moving horizon estimation scheme. The stability of the closed-loop system is analyzed rigorously and conditions that ensure the closed-loop stability are derived. Extensive simulations based on a chemical process example illustrate the effectiveness of the proposed approach.     

Nonlinear state estimation for suspension control applications: a Takagi-Sugeno Kalman filtering approach

A new nonlinear state estimation approach, which combines classical Kalman filter theory and Takagi-Sugeno (TS) modeling, is proposed in this paper. To ensure convergence of the TS observer, conditions are derived that explicitly account for the TS model's confined region of validity. Thereby, the secured domain of attraction (DA) of the TS error dynamics is maximized within given bounds. The TS Kalman filtering concept is then applied to a hybrid vehicle suspension configuration, whose nonlinear dynamics are exactly represented by a continuous-time TS system. The benefit of the novel estimation technique is analyzed in comparison with the well-known EKF and UKF variants in simulations and experiments of a passive and an actively controlled suspension configuration in a quarter-car set-up. Employing a real road profile as disturbance input, the TS Kalman filter shows the highest estimation quality of the concepts studied. Moreover, as its computational complexity adds up to only one third of the one involved with the classical methods, the new approach operates remarkably efficient.     

Nonparametric conditional hazard rate estimation: A local linear approach

A new nonparametric estimator for the conditional hazard rate is proposed, which is defined as the ratio of local linear estimators for the conditional density and survivor function. The resulting hazard rate estimator is shown to be pointwise consistent and asymptotically normally distributed under appropriate conditions. Furthermore, plug-in bandwidths based on normal and uniform reference distributions and minimizing the asymptotic mean squared error are derived. In terms of the mean squared error the new estimator is highly competitive in comparison to existing estimators for the conditional hazard rate. Moreover, its smoothing parameters are relatively robust to misspecification of the reference distributions, which facilitates bandwidth selection. Additionally, the new hazard rate estimator is conveniently calculated using standard software for local linear regression. The use of the local linear hazard rate is illustrated in an application to kidney transplant data.     

空气流动阻力下非线性车辆队列最优能耗控制方法

为了优化车辆队列在长距离行驶过程中的能源消耗,对空气流动阻力下车辆队列能耗优化间距策略以及相应的队列控制方法进行了研究;首先根据车辆队列在行驶过程中受到的空气流动阻力,建立基于异构风阻系数的车辆动力学模型;其次,设计基于滑模控制的非线性车辆队列控制方法,使其能够在不同风阻系数下稳定地收敛到期望的车辆队列;在此基础上,构建稳态下车辆队列能量消耗评价模型,并通过优化分析,计算能量消耗最优下的车辆队列期望车间距;最后通过数值仿真的手段验证所提控制方法的有效性与可行性;该结果表明:所设计的控制器能够使整个车辆队列达到期望的控制效果;得到的最优车间距能够使得特定条件下车辆队列稳态能量消耗降低。     

一种结合Bayes估计的NHPP航班查询软件可靠性评估方法

从航班查询系统故障的监控日志出发,分析了航班查询系统的故障数与其发生的时间间隔区间的关系,建立了NHPP模型。为了使NHPP模型能够更加准确地反映时间对航班查询系统的故障数的影响,对航班查询系统的寿命总体分布函数F(t)进行贝叶斯估计。当不重复采样时,贝叶斯估计取F(t)的先验分布为DP(α,P0),使得F(t)后验分布容易分析计算,从而使NHPP模型的估计更为精确。利用实际监控数据进行仿真,对模型进行了实现,结果表明了该模型对可靠性评估有较高的准确度。     

A hybrid cost estimation framework based on feature-oriented data mining approach

This paper presents an informatics framework to apply feature-based engineering concept for cost estimation supported with data mining algorithms. The purpose of this research work is to provide a practical procedure for more accurate cost estimation by using the commonly available manufacturing process data associated with ERP systems. The proposed method combines linear regression and data-mining techniques, leverages the unique strengths of the both, and creates a mechanism to discover cost features. The final estimation function takes the user’s confidence level over each member technique into consideration such that the application of the method can phase in gradually in reality by building up the data mining capability. A case study demonstrates the proposed framework and compares the results from empirical cost prediction and data mining. The case study results indicate that the combined method is flexible and promising for determining the costs of the example welding features. With the result comparison between the empirical prediction and five different data mining algorithms, the ANN algorithm shows to be the most accurate for welding operations.     

基于人脸角度估计的多观察视频合并

对特定人物进行观察记录时,由于被观察对象处于运动状态,需要通过布置多个观察源来充分采集其行为信息,这导致大量冗余信息的存储,同时不利于后续对视频的检索和对被测对象行为的分析.为此,提出了一种基于人脸角度估计的多观察视频合并方法,通过对多个观察视频的拆分、人脸检测、人脸角度估计和重组,获得单一的包含被观察对象正面行为的观察视频,然后进行存储.通过实验验证了算法的可行性,同时讨论了多观察源采集视频不同步带来的影响.     

基于比特估计的RFID标签数量估计算法

为了进一步提高稠密标签环境中标签估计算法的精度,在分析比较传统的基于比特标签估计算法的基础上,提出一种比特估计的优化算法。首先,基于二项分布理论,利用未被选择比特位的观测值计算空闲比特率;然后,通过确定空闲比特率的阈值,建立稠密标签环境中的标签数量估计模型;最后,推导出标签数量估计值与时隙消耗的数学表达式。仿真结果表明,改进算法的标签估计精度要优于传统的基于比特标签估计的精度,且对于不同规模的标签群,改进算法具有稳定的估计性能。