Hybrid calibration method for six-component force/torque transducers of wind tunnel balance based on support vector machines

A hybrid calibration approach based on support vector machines (SVM) is proposed to characterize nonlinear cross coupling of multi-dimensional transducer. It is difficult to identify these unknown nonlinearities and crosstalk just with a single conventional calibration approach. In this paper, a hybrid model comprising calibration matrix and SVM model for calibrating linearity and nonlinearity respectively is built up. The calibration matrix is determined by linear artificial neural network (ANN), and the SVM is used to compensate for the nonlinear cross coupling among each dimension. A simulation of the calibration of a multi-dimensional sensor is conducted by the SVM hybrid calibration method, which is then utilized to calibrate a six-component force/torque transducer of wind tunnel balance. From the calibrating results, it can be indicated that the SVM hybrid calibration method has improved the calibration accuracy significantly without increasing data samples, compared with calibration matrix. Moreover, with the calibration matrix, the hybrid model can provide a basis for the design of transducers.     

数控机床切削力负载模拟器的显式力控制算法设计

为了对数控机床进行可靠性试验,设计了一种基于3P-(4S)并联机构的负载模拟器。基于PI反馈控制的显式力控制算法,并引入了模糊控制器,提高系统对不同加载误差的适应性。在控制系统中引入了力前馈控制器,提高控制系统的动态性能,减少实际加载力的滞后。同时,由于加载目标即机床主轴的进给运动轨迹未知,因此引入了主轴位置预测前馈以进行运动补偿,减少主轴运动所带来的干扰。最后设计并实施了静态与动态负载模拟试验,验证了所提控制算法的有效性。     

星载面阵SAR多维波形编码技术研究

为解决传统高分辨和宽测绘带以及高信噪比和宽测绘带之间的矛盾,采用面阵合成孔径雷达（SAR）多维波形编码（Multi-Dimensional Waveform Coding, MDWC）技术,提出了将面阵分为多行多列的子孔径,将距离维测绘带分成多个子测绘带进行脉内扫描照射,同时对子测绘带内子脉冲信号进行频分编码,并采用低PRF发射,接收端则利用方位向多个相位中心解多普勒模糊,获得宽测绘带、高分辨与高信噪比等多项性能,仿真试验表明了方法的有效性。     

Polynomial networks based adaptive attitude tracking control for NSVs with input constraints and stochastic noises

This paper proposes a backstepping technique and Multi-dimensional Taylor Polynomial Networks (MTPN) based adaptive attitude tracking control strategy for Near Space Vehicles (NSVs) subjected to input constraints and stochastic input noises. Firstly, considering the control input has stochastic noises, and the attitude motion dynamical model of the NSVs is actually modeled as the Multi-Input Multi-Output (MIMO) stochastic nonlinear system form. Furthermore, the MTPN is used to estimate the unknown system uncertainties, and an auxiliary system is designed to compensate the influence of the saturation control input. Then, by using backstepping method and the output of the auxiliary system, a MTPN-based robust adaptive attitude control approach is proposed for the NSVs with saturation input nonlinearity, stochastic input noises, and system uncertainties. Stochastic Lyapunov stability theory is utilized to analysis the stability in the sense of probability of the entire closed-loop system. Additionally, by selecting appropriate parameters, the tracking errors will converge to a small neighborhood with a tunable radius. Finally, the numerical simulation results of the NSVs attitude motion show the satisfactory flight control performance under the proposed tracking control strategy.     

基于多维特征的战场实体目标分群方法

目标分群问题也称为群形成过程，是态势估计中的一个重点和难点问题，属于信息融合中的高层融合范畴。现有的目标分群方法大多是将空间位置相近的目标聚为一类，极少有分群方法考虑到进行战场态势分析所需的位置、速度、航向、属性和类型等多维特征。为此，首先根据海战场态势分析需求，基于目标的特征属性定义了多维度欧式距离；然后综合利用分类与密度聚类两种分析方法，提出了一种战场实体目标分群方法,以综合实现战场实体目标的实时分群处理；最后，将该算法应用于一个军用场景的数据集上进行仿真实验。仿真结果表明了该方法在海战场环境中实现目标分群处理的可行性和有效性。