基于图像特征分类和RBF网络的两轴车辆动态称重技术

汽车在行驶过程中的总重信号通常由各轴重信号确定,各轴重信号的测试精度取决于对车辆运动参数和振动信号等的精确分析。本文采用径向基函数(Radial basis function,RBF)网络处理轴重信号,针对该网络的泛化能力与拟合精度的矛盾,将车辆按照重量分为大、中、小3种类型,并进行整车建模和网络训练;实际测试过程中,利用汽车俯视图像提取类型特征,然后根据汽车的类型将测试参数输入不同的神经网络进行处理,以静态测量结果为相对真值。仿真结果表明,分类建模比单一混合建模具有更高的测试精度。     

基于小生境遗传自适应RBFN的歼击机故障认定方法

在模式识别领域中,如何实现更高精度的分类一直是个核心问题。本文提出了将自适应RBF神经网络与小生境遗传算法相结合的方法,其中自适应RBF神经网络通过对样本判断,自动实现对RBF网络添加新的隐层节点或者将样本归于已存在的隐层节点所属的类;小生境遗传算法用于寻找最优的网络宽度值。两者相结合最后确定一个隐层节点数与类别数相同的俭省的网络。用歼击机故障数据进行仿真,比较结果表明此方法能实现更高精度的故障认定。     

采用PQLF的不确定模糊系统最优保性能控制

对一类模有界的参数不确定T-S模糊系统的保性能控制问题进行了分析研究。一个二次指标函数表征了闭环系统的性能。采用分段二次Lyapunov函数(Piecewise quadratic Lyapunov function,PQLF)方法,在使闭环系统渐近稳定的前提下以线性矩阵不等式(Linear matrix inequality,LMI)的形式给出了一种鲁棒最优保性能控制律。同时提出了一种新型分段并行补偿(Parallel distributed compensation,PDC)控制策略。该PDC控制器同样反映了模糊系统前提变量的规则结构信息。数值仿真显示,采用该设计方法所得到的闭环保性能值优于传统二次Lyapunov函数(Common quadratic Lyapunov function,CQLF)方法下的闭环保性能值,且系统动态性能良好,鲁棒性强。     

A variational Bayesian-based robust adaptive filtering for precise point positioning using undifferenced and uncombined observations

In the application of precise point positioning (PPP), especially in the dynamic mode, the classical Kalman filter (KF) usually produces a large number of estimation errors or diverges when there are gross errors in the observation data or unexpected turbulences occur in target motion state or both of them. For such problem, a variational Bayesian (VB)-based robust adaptive Kalman filtering (VB-RAKF) is proposed in this paper. This filter introduces a classification robust equivalent weight function to resist observation gross error and the inverse Wishart prior to model inaccurate process noise covariance matrix (PNCM). To improve the instantaneous accuracy of state estimation, the VB approach is used to obtain better estimations of inaccurate PNCM. Several sets of observation data collected by IGS reference stations and vehicles are employed to check the robustness and positioning accuracy of the VB-RAKF model. The results show that the VB-RAKF algorithm is more robust than the KF, and can effectively resist the gross error in observation data and control state disturbance. In the IGS reference station tests, when compared to the KF, the static positioning accuracies of the VB-RAKF in the north, east and up directions are improved by 13%, 8% and 22%, respectively, and the simulated dynamic positioning accuracies of the VB-RAKF in the north, east and up directions are improved by 19%, 9% and 21%, respectively. The in-vehicle dynamic test verifies that the VB-RAKF outperforms the KF, and shows that the VB-RAKF has better performance than the KF when dealing with observation data which has obvious gross errors, and similar performance as the KF when gross errors are small.     

Reliability estimation of rotary lip seal in aircraft utility system based on time-varying dependence degradation model and its experimental validation

With several attractive properties, rotary lip seals are widely used in aircraft utility system, and their reliability estimation has drawn more and more attention. This work proposes a reliability estimation approach based on time-varying dependence analysis. The dependence between the two performance indicators of rotary lip seals, namely leakage rate and friction torque, is modeled by time-varying copula function with polynomial to denote the time-varying parameters, and an efficient copula selection approach is utilized to select the optimal copula function. Parameter estimation is carried out based on a Bayesian method and the reliability during the whole lifetime is calculated based on a Monte Carlo method. Degradation test for rotary lip seal is conducted and the proposed model is validated by test data. The optimal copula function and optimal order of polynomial are determined based on test data. Results show that this model is effective in estimating the reliability of rotary lip seals and can achieve a better goodness of fit.     

A global empirical orthogonal function model of plasmaspheric electron content

Based on the method for establishing a global plasmaspheric model using observations from COSMIC and MetOp-A orbit determination GNSS receivers, Chen et al. (2017) obtained a global plasmaspheric total electron content product with a spatial resolution of 2.5° × 5° and a time resolution of 4 h. In this paper, we use those global plasmaspheric electron content product in 2008, 2010, 2011, 2012, 2014 for 1446 days to establish a global plasmaspheric empirical model based on empirical orthogonal function (EOF). The model can well characterize the spatiotemporal variation of plasmaspheric electron content (PEC) and the influence of solar radiation on it. Only the first four orders of EOF sequences can characterize the 98.43% features of the original PEC dataset. The principal component coefficient Pk is decomposed twice during modeling, and the combination of trigonometric function and linear function is used to model Pk to characterize the solar cycle, annual cycle, semi-annual cycle and quarter-cycle variation. We compare the PEC model values with the actual observation data, the results show that the empirical PEC model values are highly correlated with the actual observations. The correlation between the two is above 0.96, and the RMS maximum of the difference between the PEC model values and the observed values are 0.70 TECU, and the average of the difference between the PEC model values and the observed values are −0.18 TECU, respectively. In addition, we validate the reliability of the global plasmaspheric model established by two empirical orthogonal function decomposition method using actual observation data, according to the global distribution of the differences between the PEC model values and the observed values in low solar activity and high solar activity, it can be seen that under low solar activity and high solar activity conditions, the model has good adaptability.     

Estimation of differential code biases with Jason-2/3 onboard GPS observations

With the continuous deployment of Low Earth Orbit (LEO) satellites, the estimation of differential code biases (DCBs) based on GNSS observations from LEO has gained increasing attention. Previous studies on LEO-based DCB estimation are usually using the spherical symmetry ionosphere assumption (SSIA), in which a uniform electron density is assumed in a thick shell. In this study, we propose an approach (named the SHLEO method) to simultaneously estimate the satellite and LEO onboard receiver DCBs by modeling the distribution of the global plasmaspheric total electron content (PTEC) above the satellite orbit with a spherical harmonic (SH) function. Compared to the commonly used SSIA method, the SHLEO model improves the GPS satellite DCB estimation accuracy by 13.46% and the stability by 22.34%, respectively. Compared to the GPS satellite DCBs estimated based on the Jason-3-only observations, the accuracy and monthly stability of the satellite DCBs can be improved by 14.42% and 26.8% when both Jason-2 and Jason-3 onboard observations are jointly processed. Compared with the Jason-2 solutions, the GPS satellite DCB estimates based on the fusion of Jason-2 and Jason-3 observations have an improved consistency of better than 18.26% and 9.71% with the products provided by the Center for Orbit Determination in Europe (CODE) and Chinese Academy of Sciences (CAS). Taking the DCB products provided by the German Aerospace Center (DLR) as references, there is no improvement in accuracy of the GPS satellite DCB estimates based on the fusion of Jason-2 and Jason-3 observations than the Jason-2 solutions alone. A periodic variation is found in the time series of both the Jason-3 and Jason-2 onboard receiver DCB estimates. Preliminary analysis of the PTEC distribution based on the estimated SH coefficients are also presented.     

漂浮基空间机器人捕获卫星过程动力学模拟及捕获后混合体运动的RBF神经网络控制

 讨论了漂浮基空间机器人在轨捕获目标卫星过程的碰撞动力学建模,以及捕获操作结束后空间机器人与卫星混合体的稳定控制问题。首先采用多刚体动力学建模方法并结合空间机器人捕获目标卫星过程中的碰撞动力学特性,建立了漂浮基空间机器人在轨捕获漂浮卫星过程的动力学模型,并在此基础上计算出完成捕获操作后空间机器人与目标卫星混合体关节的运动速度。然后针对卫星及空间机器人系统惯性参数均是未知的复杂情况,应用上述模型、神经网络控制理论和Lyapunov稳定性理论,设计了空间机器人与卫星混合体在捕获过程碰撞冲击影响下稳定运动的高斯径向基函数神经网络控制方案,以达到对捕获卫星的有效控制。此外,高斯径向基函数神经网络控制方案具有不需要测量和反馈载体位置、移动速度与加速度的显著优点。系统数值仿真证实了上述控制方案的有效性。     

0-1线性规划的连续化求解方法

针对0-1线性规划的优化问题,提出一种惩罚函数方法。考虑到0-1线性规划的最优值特征,通过在目标函数中加上惩罚函数,将0-1离散线性规划模型连续化成非线性规划模型,并使用Matlab的Fmincon函数进行求解。经对多个算例的计算,并和其他算法比较,结果表明惩罚函数法的可行性和有效性。将该方法应用于实际的飞机排班问题上,取得比较满意的结果。     

一种求解Euler方程的简化气体动理学模型

基于一种简化的平衡分布函数,提出一种新的动理学通量矢量分裂（KineticFluxVectorSplitting,KFVS）格式来求解Euler方程。算例证实,新的KFVS格式继承了原始KFVS格式中捕捉激波准确、无数值震荡的优点。与原始的KFVS格式相比,由于简化的平衡分布函数与Maxwellian平衡分布函数相比复杂程度大大降低,计算中不需要再计算误差函数,新的KFVS格式的计算效率得到了很大提高。在同样的计算精度下,新KFVS格式比原始KFVS格式计算效率提高了一倍以上,显示了新格式具有良好的数值模拟性能。