基于出租车GPS数据的路段平均速度估计模型

为了解决现有基于GPS数据的路段平均速度估计模型应用条件苛刻、难以满足低成本和高精度信息需求问题,考虑不同类型GPS车辆运行特征,设计了两个基于出租车GPS数据估计路段平均速度的改进模型.基于两个改进模型,设计了路段平均速度的融合估计方法.用某城市局部路网的出租车GPS数据验证两个改进模型,并与传统模型进行了对比分析....     

非线性滤波器在动力定位船位估计中的应用

针对动力定位船舶在作业过程中因丢失GPS位置测量信号,从而导致动力定位系统不能正常工作,而使船舶偏离作业点的问题,设计了一种非线性滤波器,它可以利用以前保存的历史数据进行船舶位置的实时估计,从而使船舶在一定的时间内可以维持在作业点附近,当重新获得GPS信号后,又可以快速地进入正常工作状态。为了使历史数据能够更准确地反映船舶的实时位置,利用历史数据相对于作业点的平均偏差对历史数据进行了补偿。最后给出了此非线性滤波器在船舶仿真系统中的仿真曲线,分析表明在有滤波器的情况下,船舶真实位置估计的准确度比没有滤波器的情况下有明显的改善。     

Suspension system state estimation using adaptive Kalman filtering based on road classification

This paper provides a new method to solve the problem of suspension system state estimation using a Kalman Filter (KF) under various road conditions. Due to the fact that practical road conditions are complex and uncertain, the influence of the system process noise variance and measurement noise covariance on the estimation accuracy of the KF is first analysed. To accurately estimate the road condition, a new road classification method through the vertical acceleration of sprung mass is proposed, and different road process variances are obtained to tune the system’s variance for the application of the KF. Then, road classification and KF are combined to form an Adaptive Kalman Filter (AKF) that takes into account the relationship of different road process noise variances and measurement noise covariances under various road conditions. Simulation results show that the proposed AKF algorithm can obtain a high accuracy of state estimation for a suspension system under varying International Standards Organisation road excitation levels.     

Adhesion estimation at the wheel–rail interface using advanced model-based filtering

The railway industry in the UK is currently expanding the use of condition monitoring of railway vehicles. These systems can be used to improve maintenance procedures or could potentially be used to monitor current vehicle running conditions without the use of cost prohibitive sensors. This paper looks at a novel method for the online detection of areas of low adhesion in the wheel/rail contact that cause significant disruption to the running of a network, particularly in the autumn season. The proposed method uses a Kalman–Bucy filter to estimate the creep forces in the wheel–rail contact area; post-processing is then applied to provide information indicative of the actual adhesion level. The algorithm uses data that, in practice, would be available from a set of modest cost inertial sensors mounted on the vehicle bogie and wheel-sets. The efficacy of the approach is demonstrated using simulation data from a nonlinear dynamic model of the vehicle and its track interface.     

Contents Index Volume 35 and 36

Active safety systems would benefit from tyre force and friction potential information. Different sensor concepts, including, among others, the EU–funded Apollo–project developed tyre sensor based on optical position detection, are being studied. The sensor can measure tyre carcass deflections with respect to the rim. The carcass deflections can be used to calculate tyre forces and they may be exploited in the estimation of friction potential. The waveforms of the sensor signal are illustrated. The vertical and lateral force estimations are presented with unavoidable compensation parts. The tyre sensor measurements were compared to the measurement–vehicle results and good correlations achieved. Continuing activities are concerned with the estimation of friction potential and the detection of aquaplaning.     

基于应力松弛试验用有限元预估车辙

为了使车辙预估变得更为简便,提出一种基于应力松弛试验的改进的有限元车辙预估方法。研究结果表明：不仅从粘弹性力学的角度分析,应力松弛试验在理论上与Maxwell模型具有一致性,而且实例计算结果也验证了此方法的可行性。该方法便于不熟悉本构理论的工程人员进行车辙预估。     

基于卡尔曼滤波算法的电机转速精确估计

从无刷直流电机入手建立数学模型，利用卡尔曼Kalman（EKF）滤波原理算法，对无刷直流电机的转速进行精确估计，然后在Matlab环境下仿真，验证卡尔曼滤波算法对电机转速估计的精确性。     

An inverse finite element method for the analysis of VIV data

This paper presents an algorithm for the frequency domain solution of dynamic linear “inverse” problems, that is for the processing of measurement data (strain, acceleration etc.) acquired on a mechanical structure, in order to estimate the loads acting on the structure and its corresponding response. The problem is formulated as a constrained (force equilibrium) optimization (small deviations from measurements, small loads) problem, which is transformed into an unconstrained problem, then into differential equations. The algorithm is applied to the estimation of hydrodynamic forces induced by the shedding of vortices from an offshore oil riser.     

Road roughness estimation based on discrete Kalman filter with unknown input

ABSTRACT

The road roughness acts as a disturbance input to the vehicle dynamics, and causes undesirable vibrations associated with the ride and handing characteristics. Furthermore, the accurate measurement of road roughness plays a key role in better understanding a vehicle dynamic behaviour and active suspension control systems. However, the direct measurement by laser profilometer or other distance sensors are not trivial due to technical and economic issues. This study proposes a new road roughness estimation method by using the discrete Kalman filter with unknown input (DKF-UI). This algorithm is built on a quarter-car model and uses the measurements of the wheel stroke (suspension deflection), and the acceleration of the sprung mass and unsprung mass. The estimation results are compared to the measurements by laser profilometer in-vehicle test.     

铁路枢纽折角车流数值变化的动态模拟

以解编车流作业规则和系统资源限制为约束,构造不同目标函数下铁路枢纽编组站作业分工数学模型,给出折角车流理论最大值和最小值及相应编组站作业分工方式及车流条件。基于折角车流变化的复杂性和多样性,将外部车流环境视为一个动态的随机输入,以特定铁路枢纽为背景用计算机模拟技术和遗传算法计算不同条件下折角车流数值解。通过对模拟数据趋势变化比较分析,提出铁路枢纽运输组织不应过分追求折角车流最小等结论。