反射式全光纤激光测速法在气炮实验中的应用

建立了用于一级轻气炮弹速测量的全光纤激光光束反射测量系统(All Fiber Optical Beam Reflectance简称AFOBR)，进行了弹速动态测量实验，并与传统的电探针测速技术进行对比。结果表明：与传统的电探针测速技术相比，AFOBR测速系统具有较高的测速精确度和可靠性，其速度测量的相对扩展不确定度小于等于0．5％。     

新型陀螺仪转子表面的测量及对陀螺性能的影响

提出了一种新的陀螺转子表面测量技术，它采用光强调制式的单光纤传感器作为测量元件。系统测试(0～100μ)结果显示：相应的灵敏度为2．5mV／μm，精度等级优于1％，重复性优于0．5％。设计了一种适用于陀螺转子表面测量的系统，其测量精度达到0．005μm。以直径38mm的实心陀螺转子球为例介绍了整个系统的组成及其工作过程，给出了两种转子表面图形绘制法(墨克脱投影法和三维表面重现法)和该陀螺转子的三维表面测量图；最后分析了图形的存在引起的转子表面不规则对陀螺性能的负影响，这些参数可以用来预测陀螺漂移性能。陀螺；转子表面测量；光纤传感器；二维旋转台；表面图的绘制     

基于BP网络的地磁基准图制备及其精度评价

利用测量数据建模和插值是地磁基准图制备的两种基本方法,常用的地磁场建模方法不适合小尺度的区域地磁场建模.人工神经网络具有良好的逼近任意复杂非线性系统的能力,本文引入应用最为广泛的反向传播(Backward Promulgation,BP)神经网络建立区域地磁场的模型,将地理经度和纬度作为输入,地磁场总强度值作为输出,通过部分测量数据对网络进行训i练,用其余测量数据对网络的性能进行评价,进而由训练好的网络得到基准图制备需要的地磁数据.分别使用BP网络建模方法和8种插值方法在不同数据点分布情况下进行了基准图的制备实验,精度评价结果表明了BP网络建模方法的有效性.根据实验结果对不同数据点分布情况下各种方法的优劣进行了比较.     

多相机3D-DIC及其在高温变形测量中的应用

将基于双目视觉的三维数字图像相关方法 (Three-dimension digital image correlation,3D-DIC)与多相机同步采集系统相结合,形成基于多相机的3D-DIC系统。依据三维空间误差(Three-dimensional residual,3Dresidual)最小原则,确定各点对应的最佳双目视觉系统,获得物体全场三维变形。以四相机3D-DIC系统为例,与测量精度达10~20nm的电子散斑干涉测量系统同时对平板的离面位移进行测量,并对测量得到的离面位移最大值进行了对比分析。结果显示,荷载较小时,四相机3D-DIC与电子散斑干涉测量系统误差稍大,最大达到2.7%;荷载增大,物体变形增大时,两种测量系统结果基本相同。文中讨论了四相机测量系统的不稳定对实验结果的影响。利用该四相机3D-DIC系统对镍合金不锈钢材料在高温场中的变形进行测量,获得了物体的三维变形场,并分析了材料的膨胀系数,得到了试件的热应变-温度曲线和膨胀系数随温度变化的关系式。     

数字图像相关方法若干关键问题研究进展

本文报道了作者及其所在课题组近期在数字图像相关(DIC)测量方法上取得的重要研究进展。主要包括:(1)通过对DIC方法中反向组合高斯-牛顿算法的理论误差分析,提出了新的理论误差公式,进一步证明了反向组合高斯-牛顿算法在提高计算速度和测量精度方面的综合优势;(2)采用提出的理论误差公式,发展了数字散斑场的优化及制作方法,保证了测量结果的一致性和正确性;(3)基于相机阵列和图像拼接技术,发展了超分辨率数字图像相关方法,大大提高了DIC测量方法的应变测量分辨率;(4)提出了大视场条件下的三维系统标定方法,实现了三维测量系统的外参实时标定和多相机测量系统中相机位姿的自动矫正;(5)研制了便携式原位三维测量仪和多尺度DIC测量系统,实现了三维实时数字图像相关测量,进一步满足了DIC方法在工业在线检测和医学领域中的应用需求。     

远距离,高精度二维动态位移测量

本文介绍了基于ＣＣＤ位置探测技术的远距离位移测量系统。将一明亮光标固定于被测目标位置上，用望远显微成象系统将光标成象于ＣＣＤ的光靶上，ＣＣＤ摄像机将靶面上的光信号转变为相应的电信号，利用数字图象处理技术可以精确地确定光标的位置。通过与初始位置的比较，可以测量被测目标所产生的微小位移。在６ｍ的测量距离上可获得０．０１ｍｍ的测量精度，而且通过改变光学系统可使测量距离扩大到１００ｍ甚至更远     

模糊神经网络预测在卫星姿态测量中的应用

以满足对地观测卫星测姿精度为目标,将由惯性基准、红外地平仪和太阳敏感器测姿过程视为典型的建模问题,讨论了基于自适应神经网络的模糊推理系统(ANFIS)的卫星姿态预测。仿真结果表明,ANFIS预测能够满足卫星姿态测量精度的要求,具有较强的容错性,同时该方法可将俯仰、横滚和偏航三个姿态分离建模,有利于提高卫星姿态测量的可靠性,为卫星姿态测量信息处理提供了一种新的方法。     

旋转式重力梯度测量系统试验及数据处理

旋转式重力梯度测量系统采用旋转调制方式求取重力梯度信息。首先,从旋转加速度计的基本原理出发,给出了重力梯度测量系统的主要工作模式;其次,构建了旋转加速度计重力梯度测量系统组成和主要功能模块,提出了采用引力产生装置开展实验室引力梯度测量的试验方案;最后,给出了旋转加速度计重力梯度测量系统的静态梯度试验验证基本条件、试验设备,并开展了重力梯度测量试验。试验结果表明,旋转式重力梯度测量系统在实验室条件下完成引力梯度试验,该系统可以检测优于200 Eu(1 Eu=10~(-9)/s~2)的引力梯度,该系统开展的试验验证为动态重力梯度仪的研制奠定了基础。     

静电陀螺仪转子的超精密圆度测量

本文在比较了误差分离的几种方法之后,着重讨论了多步法的方法误差和系统误差。按此法建立了计算机辅助毫微米圆度测量系统,将圆度仪的测量精度(2σ)从原来的0.025微米提高到0.002微米,足以满足精密工程、惯性技术和宇航科学等领域对超精圆球的计量要求。     

四种TEK数字示波器性能的比较

对物理实验中常用的阿(公司的四种型号数字示波器延迟时间抖动进行测量，给出的四种阿(数字示波器延迟时间差异，有助于设计实验测试系统时正确选型，并作为测试系统中的一部分修正量对实验测量结果进行修正。     

基于RBF网络的有源噪声控制

提出一种神经自适应噪声有源控制（ANC）的方法。应用RBF（Radial Basis Function)网络对噪声进行有源控制。针对RBF的网络特点,使用递阶遗传算法确定网络参数（连接权、隐节点中心和宽度），同时解决了网络拓扑结构的优化训练。利用RBF网络的有源噪声控制系统用于三维空间传播的宽频带空调噪声的降噪获得了良好的效果。     

Structural reliability analysis for implicit performance function using radial basis function network

This is the second paper of our work on structural reliability analysis for implicit performance function. The first paper proposed structural reliability analysis methods using multilayer perceptron artificial neural network [Deng, J., Gu, D.S., Li, X.B., Yue, Z.Q., 2005. Structural reliability analysis for implicit performance function using artificial neural network. Structural Safety 25 (1), 25–48]. This paper presents three radial basis function network (RBF) based reliability analysis methods, i.e. RBF based MCS, RBF based FORM, and RBF based SORM. In these methods, radial basis function network technique is adopted to model and approximate the implicit performance functions or partial derivatives. The RBF technique uses a small set of the actual data of the implicit performance functions, which are obtained via physical experiments or normal numerical analysis such as finite element methods for the complicated structural system, and are used to develop a trained RBF generalization algorithm. Then a large number of the function values and partial derivatives of implicit performance functions can be readily obtained by simply extracting information from the established and successfully trained RBF network. These function values and derivatives are used in conventional MCS, FORM or SORM to constitute RBF based reliability analysis algorithms. Examples are presented in the paper to illustrate how the proposed RBF based methods are used in structural reliability analysis. The results are well compared with those obtained by the conventional reliability methods such as the Monte-Carlo simulation, multilayer perceptrons networks, the response surface method, the FORM method 2, and so on. The examples showed the proposed approach is applicable to structural reliability analysis involving implicit performance functions.     

Adaptive neural tracking control for a class of perturbed pure-feedback nonlinear systems

This paper focuses on the problem of the adaptive neural control for a class of a perturbed pure-feedback nonlinear system. Based on radial basis function (RBF) neural networks’ universal approximation capability, an adaptive neural controller is developed via the backstepping technique. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the tracking error eventually converges to a small neighborhood around the origin. The main advantage of this note lies in that a control strategy is presented for a class of pure-feedback nonlinear systems with external disturbances being bounded by functions of all state variables. A numerical example is provided to illustrate the effectiveness of the suggested approach.     

Direct adaptive neural control for strict-feedback stochastic nonlinear systems

This note considers the problem of direct adaptive neural control for a class of nonlinear single-input/single-output (SISO) strict-feedback stochastic systems. The variable separation technique is introduced to decompose the coefficient functions of the diffusion term. Radical basis function (RBF) neural networks are used to approximate unknown and desired control signals, then a novel direct adaptive neural controller is constructed via backstepping. The proposed adaptive neural controller guarantees that all the signals in the closed-loop system remain bounded in probability. A main advantage of the proposed controller is that it contains only one adaptive parameter needed to be updated online. Simulation results demonstrate the effectiveness of the proposed approach.     

Integrated radial basis functions‐based differential quadrature method and its performance

In this paper, indirect radial basis function networks (IRBFN) proposed by Nam and Tranh (Neural Networks 2001; 14 (2):185–199; Appl. Math. Modelling 2003; 27 :197–220) are incorporated into the differential quadrature (DQ) approximation of derivatives. For simplicity, this new variant of RBF‐DQ approach is named as iRBF‐DQ method. The proposed approach is validated by its application to solve the one‐dimensional Burger's equation, and simulate natural convection in a concentric annulus by solving Navier–Stokes equations. It was found that as compared to the benchmark data, the iRBF‐DQ approach can provide more accurate results than the original RBF‐DQ method. Copyright © 2006 John Wiley & Sons, Ltd.     

Nonlinear vibration semi-active control of automotive steering using magneto-rheological damper

Traffic accidents are often caused by vibration of automotive steering because the vibration can make a vehicle run like a snake. A?novel semi-active vibration control strategy of automotive steering with magneto-rheological (MR) damper is proposed in this paper. An adaptive RBF neural sliding mode controller is designed for the vibration system. It is showed that an equivalent dynamic model for the vibration system is established by using Lagrange method, and then treats it as actual system partially. A?feedback control law is designed to make this nominal model stable. Uncertain part of system and outside disturbance are estimated using RBF neural network, and their upper boundary is obtained automatically. By constructing reasonable switch function, state variables can arrive at origin asymptotically along the sliding mode. Strong robust character of control system is proved by stability analysis and a numerical simulation example is performed to support this control scheme.     

Adaptive neural network control for coordinated motion of a dual-arm space robot system with uncertain parameters

Control of coordinated motion between the base attitude and the arm joints of a free-floating dual-arm space robot with uncertain parameters is discussed. By combining the relation of system linear momentum conversation with the Lagrangian approach, the dynamic equation of a robot is established. Based on the above results, the free-floating dual-arm space robot system is modeled with RBF neural networks, the GL matrix and its product operator. With all uncertain inertial system parameters, an adaptive RBF neural network control scheme is developed for coordinated motion between the base attitude and the arm joints. The proposed scheme does not need linear parameterization of the dynamic equation of the system and any accurate prior-knowledge of the actual inertial parameters. Also it does not need to train the neural network offline so that it would present real-time and online applications. A planar free-floating dual-arm space robot is simulated to show feasibility of the proposed scheme.     

动态环境下光纤陀螺误差辨识与补偿技术

为了提高光纤陀螺在高动态环境下的测量精度,需要精确地辨识角加速度信息以便有效地补偿。针对直接对陀螺的角速度信息微分处理后得到角加速度的方法误差较大的问题,提出了将微分后的角加速度信息分为线性和非线性两个部分,其中线性部分采用Savitzky-golay最小二乘拟合,而非线性部分则采用RBF神经网络技术进行拟合。上述处理方法能更真实地反映实际物理过程,具有较强的自适应性和较好的拟合效果。通过试验验证,证明了该方法的有效性和准确性,提高了角加速度辨识精度,比直接微分的方法测量精度提高二个数量级,有效地补偿了陀螺仪在高动态环境下的测量精度。     

基于人工神经网络的非结构网格尺度控制方法

网格自动化生成和自适应是制约计算流体力学发展的瓶颈问题之一, 网格生成质量、效率、灵活性、自动化程度和鲁棒性是非结构网格生成的关键问题. 在非结构网格生成中, 网格空间尺度分布控制至关重要, 直接影响网格生成质量、效率和求解精度. 采用传统的背景网格法进行空间尺度分布控制需要在背景网格上求解微分方程得到背景网格上的尺度分布, 再将网格尺度从背景网格插值到真实空间点, 过程十分繁琐且耗时. 本文从效率和自动化角度提出两种网格尺度控制方法, 首先发展了基于径向基函数(RBF)插值的网格尺度控制方法, 通过贪婪算法实现边界参考点序列的精简, 提高了RBF插值的效率. 同时, 还采用人工神经网络进行网格尺度控制, 初步引入相对壁面距离和相对网格尺度作为神经网络输入输出参数, 建立人工神经网络训练模型, 采用商业软件生成二维圆柱和二维翼型非结构三角形网格作为训练样本, 通过训练和学习建立起相对壁面距离和相对网格尺度的神经网络关系. 进一步实现了二维圆柱、不同的二维翼型的尺度预测, RBF方法和神经网络方法的效率与传统背景网格法相比提高了5~10倍, 有助于提高网格生成的效率. 最后, 将方法推广应用于各向异性混合网格尺度预测, 得到的网格质量满足要求.      

Decentralized adaptive neural control of nonlinear systems with unknown time delays

In this paper, a novel decentralized adaptive neural control scheme is proposed for a class of uncertain multi-input and multi-output (MIMO) nonlinear time-delay systems. RBF neural networks (NNs) are used to tackle unknown nonlinear functions, then the decentralized adaptive NN tracking controller is constructed by combining Lyapunov–Krasovskii functions and the dynamic surface control (DSC) technique along with the minimal-learning-parameters (MLP) algorithm. The proposed controller guarantees semi-global uniform ultimate boundedness (SGUUB) of all the signals in the closed-loop large-scale system, while the tracking errors converge to a small neighborhood of the origin. An advantage of the proposed control scheme lies in that the number of adaptive parameters for each subsystem is reduced to one, and three problems of “computational explosion,” “dimension curse” and “controller singularity” are solved, respectively. Finally, a numerical simulation is presented to demonstrate the effectiveness and performance of the proposed scheme.