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.     

基于双目视觉计算的车辆跟驰状态实时感知系统

双目视觉技术能够实现目标的识别与距离计算，在自动驾驶领域有很大的应用空间。然而，现阶段双目视觉存在光照干扰、遮挡、弱纹理区域歧义匹配等问题，影响其测量的准确性和可靠性。提出基于双目视觉的跟驰状态实时感知系统，该系统采用基于车辆跟驰模型的扩展卡尔曼滤波方法对车辆跟驰状态进行实时估计，包括跟驰距离、前后车速度差等。通过实际道路试验，证明了该系统能够识别并修正测量数据中的异常值，解决弱纹理区域误匹配问题。试验结果表明：25 mm焦距与12 mm焦距的双目系统跟驰间距测量值的平均误差分别为2.66%与9.14%；在相对速度测量方面，2种焦距系统的测量精度基本相同，平均误差均为1 m·s^-1左右。所提出的方法在自动驾驶车辆环境感知领域有较好的应用前景。     

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。     

内置式永磁同步电动机无传感器速度控制和转子初始位置估计方法的应用及实验研究

针对内置式永磁同步电动机传动提出了一种新的无速度传感器控制和转子初始位置估计方法.在内置式永磁同步电动机旋转过程中,采用扩展卡尔曼滤波方法,仅需测量电机定子的电压和电流,便可得到转速和转子位置的估计值.对内置式永磁同步电动机,采用扩展卡尔曼滤波方法的主要困难在于在静止坐标系中其动态模型的复杂性.由于磁路的不对称性,在静止坐标系中建内置式永磁同步电动机的模型比建表面式永磁同步电动机模型更复杂.在无传感器的传动系统中,电机的起动过程是一个问题,因为在起动之前没有任何信息可以获得.转子初始位置估计是在电机静止时,在瞬间对定子绕组施加合适的电压脉冲序列,通过测量峰值电流获得转子位置信息.利用磁饱和效应对凸极性的影响来区分南北磁极.给出基于浮点数字信号处理器TMS320C31平台得到的内置式永磁同步电动机实验结果.     

精确反馈线性化在船舶航向保持器上的应用

现有的船舶航向保持器的设计大都采用Nomoto线性模型，而忽略了船舶操纵运动的非线性因素，因此航向控制的精度比较低。选择状态反馈精确线性化方法，将响应型船舶运动的非线性数学模型进行线性化，再利用基于闭环增益成形算法的鲁棒控制器进行控制，然后在Maflab的Simulink下进行系统仿真，仿真结果表明，所设计的基于闭环增益成形算法的控制器比基于LQR的控制器具有更好的控制能力。     

Performance estimation model of a torque converter part I: Correlation between the internal flow field and energy loss coefficient

The objective of this paper is to improve the performance estimation model of the internal flow field of a torque converter. Compared with performance experiment results, the converter based on the one-dimensional model does not satisfy the performance requirements demanded in practice. Therefore, we need to develop more predictable and reliable performance estimation models. In order to obtain shape information on three-dimensional blade geometry, a process of reverse engineering conducts a torque converter assembly, impeller, turbine and stator. In addition, a CFD simulation including mesh generation and post-processing was carried out to extract equivalent parameters from the internal flow field. The internal flow field can be explained by analyze the correlation between a performance estimation model and CFD analysis. The equivalent performance model adopts the variation of energy loss coefficients for a given operating condition according to the application of a changing energy loss coefficient by the least mean squares method. The estimated equivalent model improves the agreement in performance between experiments and the theoretical model. This model can reduce the error to within about 3 percent. Furthermore, this procedure for predicted performance achieves eminence in the estimation of the capacity factor.     

Real-time freeway traffic state estimation based on extended Kalman filter: Adaptive capabilities and real data testing

This paper reports on real data testing of a real-time freeway traffic state estimator, with a particular focus on its adaptive capabilities. The pursued general approach to the real-time adaptive estimation of complete traffic state in freeway stretches or networks is based on stochastic macroscopic traffic flow modeling and extended Kalman filtering. One major innovative feature of the traffic state estimator is the online joint estimation of important model parameters (free speed, critical density, and capacity) and traffic flow variables (flows, mean speeds, and densities), which leads to three significant advantages of the estimator: (1) avoidance of prior model calibration; (2) automatic adaptation to changing external conditions (e.g. weather and lighting conditions, traffic composition, control measures); (3) enabling of incident alarms. These three advantages are demonstrated via suitable real data testing. The achieved testing results are satisfactory and promising for subsequent applications.