SUBSPACE METHOD FOR BLIND IDENTIFICATION OF CDMA TIME-VARYING CHANNELS

A new blind method is proposed for identification of CDMA Time-Varying (TV)channels in this paper. By representing the TV channel's impulse responses in the delay-Doppler spread domain, the discrete-time canonical model of CDMA-TV systems is developed and a subspace method to identify blindly the Time-Invariant (TI) coordinates is proposed. Unlike existing basis expansion methods, this new algorithm does not require .estimation of the base frequencies, neither need the assumption of linearly varying delays across symbols. The algorithm offers definite explanation of the expansion coordinates. Simulation demonstrates the effectiveness of the algorithm.

IMPROVED COVARIANCE DRIVEN BLIND SUBSPACE IDENTIFICATION METHOD

An improved covariance driven subspace identification method is presented to identify the weakly excited modes. In this method, the traditional Hankel matrix is replaced by a reformed one to enhance the identifiability of weak characteristics. The robustness of eigenparameter estimation to noise contamination is reinforced by the improved Hankel matrix, in combination with component energy index （CEI） which indicates the vibration intensity of signal components, an alternative stabilization diagram is adopted to effectively separate spurious and physical modes. Simulation of a vibration system of multiple-degree-of-freedom and experiment of a frame structure subject to wind excitation are presented to demonstrate the improvement of the proposed blind method. The performance of this blind method is assessed in terms of its capability in extracting the weak modes as well as the accuracy of estimated parameters. The results have shown that the proposed blind method gives a better estimation of the weak modes from response signals of small signal to noise ratio （SNR）and gives a reliable separation of spurious and physical estimates.

Identification of the state-space model and payload mass parameter of a flexible space manipulator using a recursive subspace tracking method

The on-orbit parameter identification of a space structure can be used for the modification of a system dynamics model and controller coefficients. This study focuses on the estimation of a system state-space model for a two-link space manipulator in the procedure of capturing an unknown object, and a recursive tracking approach based on the recursive predictor-based subspace identification(RPBSID) algorithm is proposed to identify the manipulator payload mass parameter. Structural rigid motion and elastic vibration are separated, and the dynamics model of the space manipulator is linearized at an arbitrary working point(i.e., a certain manipulator configuration).The state-space model is determined by using the RPBSID algorithm and matrix transformation. In addition, utilizing the identified system state-space model, the manipulator payload mass parameter is estimated by extracting the corresponding block matrix. In numerical simulations, the presented parameter identification method is implemented and compared with the classical algebraic algorithm and the recursive least squares method for different payload masses and manipulator configurations. Numerical results illustrate that the system state-space model and payload mass parameter of the two-link flexible space manipulator are effectively identified by the recursive subspace tracking method.

Subspace identification for a stochastic model of plague

In this paper, a stochastic model of plague is first studied by subspace identification. First, the discrete model of plague is obtained based on the classical model. The corresponding stochastic model is proposed for the existence of stochastic disturbances. Second, for the model, the parameter matrices and noise intensity are obtained. Finally, the simulations of the model show that the subspace identification is more precise than least square method.

Subspace identification for continuous-time errors-in-variables model from sampled data

We study the subspace identification for the continuous-time errors-in-variables model from sampled data.First,the filtering approach is applied to handle the time-derivative problem inherent in continuous-time identification.The generalized Poisson moment functional is focused.A total least squares equation based on this filtering approach is derived.Inspired by the idea of discrete-time subspace identification based on principal component analysis,we develop two algorithms to deliver consistent estimates for the continuous-time errors-in-variables model by introducing two different instrumental variables.Order determination and other instrumental variables are discussed.The usefulness of the proposed algorithms is illustrated through numerical simulation.

Subspace-based identification of discrete time-delay system

We investigate the identification problems of a class of linear stochastic time-delay systems with unknown delayed states in this study. A time-delay system is expressed as a delay differential equation with a single delay in the state vector. We first derive an equivalent linear time-invariant(LTI) system for the time-delay system using a state augmentation technique. Then a conventional subspace identification method is used to estimate augmented system matrices and Kalman state sequences up to a similarity transformation. To obtain a state-space model for the time-delay system, an alternate convex search(ACS) algorithm is presented to find a similarity transformation that takes the identified augmented system back to a form so that the time-delay system can be recovered. Finally, we reconstruct the Kalman state sequences based on the similarity transformation. The time-delay system matrices under the same state-space basis can be recovered from the Kalman state sequences and input-output data by solving two least squares problems. Numerical examples are to show the effectiveness of the proposed method.

非线性结构系统的局部连接刚度辨识方法研究

连接结构在工程应用领域广泛存在,连接位置处的局部非线性特征对结构系统的动力学建模与特性预示具有重要影响。针对非线性结构系统局部连接参数未知或难以测量的问题,本文从结构动力学反问题的角度出发,提出一种基于频响函数变换的局部线性连接刚度辨识方法,通过与时域非线性子空间辨识方法相结合,可进一步获取非线性结构系统的局部线性和非线性连接刚度。设计并搭建三自由度结构系统的数值算例和实验系统,开展辨识方法的数值和实验验证,结果表明,所提方法能够分离并辨识得到非线性结构系统的标称线性系统频响函数和非线性参数,进而实现局部线性和非线性连接刚度的联合辨识。

采用贝叶斯理论的渡槽结构有限元模型更新方法

渡槽在服役过程中,其结构状态受到多种因素影响会发生改变,在进行有限元分析时,建模所用参数并不是结构实时参数。因此,需要通过监测结构的动力响应来对结构的有限元模型参数进行更新,达到准确评价结构实际运行状态的目的。提出了一种基于贝叶斯理论的有限元模型更新方法,该方法以确定性模型更新结果作为不确定性更新的初始模型参数值,使用MH算法得到模型参数的后验分布,通过ABAQUS有限元模型与MATLAB更新程序的交互访问,实现复杂结构有限元模型参数的自动更新。开展了渡槽模型试验,利用所提方法对渡槽有限元模型进行更新。结果表明,以确定性更新结果作为贝叶斯更新的参数初始值,有限元模型更新的计算效率提高了25%,更新后的误差为0.19%~6.42%,该更新方法能有效改善有限元模型的预测能力。

基于子空间辨识方法的建筑群热负荷预测研究

根据建筑热力模型和热负荷计算模型建立了状态空间模型,分别利用未指定状态变量和指定状态变量的子空间辨识方法构建了建筑群热负荷预测模型,并以哈尔滨市某小区换热站为例,验证和比较了2种方法的热负荷预测结果及训练策略和预测时长对预测精度的影响。结果表明:指定状态变量比未指定状态变量的子空间辨识方法的热负荷预测平均绝对百分比误差降低了10.00%;未指定状态变量的子空间辨识方法的中期预测效果优于短期预测,平均绝对百分比误差降低了13.34%。

基于IECSDE算法的PEMFC改进分数阶子空间辨识模型

为准确描述质子交换膜燃料电池(PEMFC)在其发电过程中的特性及变量影响关系,提出一种基于信息交流布谷鸟搜索差分进化(IECSDE)算法的改进分数阶子空间辨识方法来建立PEMFC分数阶模型。首先基于状态空间方程建立PEMFC模型,为了描述PEMFC的分数阶特性,将分数阶微分理论融入到模型中,引入Poisson滤波函数预处理实验数据,解决数据多阶不可导的问题,同时引入变步长记忆法处理分数阶微分时的权系数,提高子空间辨识精度。其次在辨识过程中的参数对于建模效果具有重大影响,因此基于IECSDE算法并对其进行优化,对布谷鸟搜索(CS)算法中的控制参数进行自适应处理,受到粒子群优化(PSO)算法的启发,改进随机游走方式提高收敛精度和速度,并引入差分进化(DE)算法与改进CS算法分别对种群进行优化,同时在寻优过程中进行信息交流提高种群的多样性和算法的鲁棒性。仿真结果表明,IECSDE算法的寻优能力在8种测试函数下比其他5种优化算法至少提升了10倍;通过对PEMFC测控平台收集到的实验数据进行模型辨识,所建立的模型将误差缩小到基于短记忆法的分数阶子空间辨识方法误差的20%,输出功率误差控制在0~0.1之间,输出电压误差控制在0~0.2之间,能够精准地模拟PEMFC发电过程。

Novel Model of Proton Exchange Membrane Fuel Cell with Predictive Control Using Hildreth Algorithm Based on Fractional-order Dynamic Model

Considering the multivariable and fractional-order characteristics of proton exchange membrane fuel cells(PEMFCs),a fractional-order subspace identification method(FOSIM)is proposed in this paper to establish a fractionalorder state space(FOSS)model,which can be expressed as a multivariable configuration with two inputs,hydrogenflow rate and stack current,and two outputs,cell voltage and power.Based on this model,a novel constrained optimal control law named the Hildreth model predictive control(H-MPC)strategy is created,which employs a Hildreth quadratic programming algorithm to adjust the output power of fuel cells through adaptively regulating hydrogen flow and stack current.dSPACE semi-physical simulation results demonstrate that,compared with proportional-integral-derivative and quadratic programming MPC(QP-MPC),the proposed H-MPC exhibits better tracking ability and strong robustness against variations of PEMFC power.

考虑相机运动补偿的视觉模态测试方法

基于计算机视觉的结构模态测试方法因其非接触式的特点和全场测量的优势而备受关注,但此类方法对相机运动敏感,通常要求相机固定以保证测量精度。本文提出一种考虑相机运动补偿的视觉模态测试方法,采用基于特征匹配的单应变换对原抖动视频进行校正,补偿相机的六自由度运动以得到稳定视频;在结构表面设置“虚拟视觉传感器”,利用欧拉视角的稠密光流法估计结构的振动响应;通过随机子空间法识别结构的模态参数。为验证所提方法的有效性,通过手持智能手机拍摄的方式采集模拟抖动的视频,并开展悬臂梁的单频振动试验和模态辨识试验。将视觉模态测试结果与扫描式激光多普勒测振仪的测试结果进行比较,前五阶模态频率和阻尼比的平均误差分别低于0.4%和11.5%,模态振型的MAC矩阵对角值高于98%。试验结果表明,所提方法有效地消除了相机运动的干扰,并获得了良好的结构模态参数估计精度。

基于变遗忘因子的空间太阳能电站时变频率辨识

考虑空间太阳能电站(SSPS)在轨运行时帆板旋转所导致的系统变构型特征,提出一种变遗忘因子广义子空间跟踪器(VFF-GYAST)递推辨识方法,在轨辨识该时变动力学系统的模态频率参数,以提高GYAST方法对于这类时变系统的跟踪能力。基于模态综合技术和子结构方法,建立多旋转关节构型SSPS(MJ-SSPS)的时变姿态-振动耦合动力学方程;根据投影子空间理论,通过计算系统均方差来确定递推过程中的时变遗忘因子,以提高GYAST方法的跟踪性能并辨识得到系统的时变伪模态频率参数。数值仿真结果表明:所提方法能够有效地识别该大型柔性系统的时变频率参数。与传统的递推子空间方法相比,所提方法具有较高的抗噪声干扰能力,在低量测信噪比时所提方法对于频率辨识结果的相对误差的平均值小于5%,且对时变系统的跟踪性能优于基于固定遗忘因子的原始方法。

从响应信号辨识斜拉桥模型的模态参数

针对斜拉桥模型在平稳随机激励下的模态参数辨识问题 ,研究了提高子空间方法参数辨识精度和可信度的有效途径。在矩阵奇异值分解的基础上 ,文中使用信号特征分量的能量指标来估计模型阶次 ,同时给出一种特征频率随拟合数据变化的稳定图。在虚假特征显示能力以及降低数值运算量等方面 ,这种形式的稳定图与能量指标的结合具有正更大的优越性。在斜拉桥模型的模态参数辨识中 ,物理模态和寄生模态得到了很好的分离 ,而且斜拉桥模型在分析频带内的物理模态被全部识别出来。辨识结果的比较说明了给出的辨识方法是有效的。

基于子空间技术的电动汽车电池模型辨识研究

电动汽车动力电池模型的观测和辨识是电动汽车高效、稳定、安全运行的基础。基于电池的电路模型,从利于辨识的角度,建立了状态空间形式的电池模型,并提出了基于子空间技术的模型辨识算法。在不同阶次、脉宽和幅值下,采用逆M序列对电池模型辨识效果进行了验证和比较,最终提出了最优的电动汽车动力电池参数辨识方法。实验结果表明,该方法具有较高的精度和普遍的适用性,可以广泛地应用于不同类型的电池。

一种新的基于遗忘因子的递推子空间辨识算法

针对工业系统中广泛存在的时变特性,提出一种新的递推子空间辨识算法,实现对系统状态空间模型的在线递推估计.为更好地跟踪系统时变特性,研究基于遗忘因子的输入输出数据矩阵构造机制,以提高递推算法的收敛速度;针对算法中奇异值分解的求解问题,将梯度型算法引入基于遗忘因子的状态子空间跟踪中,实现对广义能观测矩阵的估计,避免了子空间近似带来的估计有偏性;该算法计算简单有效,且对初值具有更高的鲁棒性;最后给出该递推算法的性能分析,理论证明其收敛性,并通过仿真实例验证算法的有效性.

模糊聚类算法稳定图应用于桥梁结构参数识别

将模糊聚类算法应用于稳定图理论,并将该稳定图与协方差驱动的随机子空间法相结合,进行桥梁结构模态参数识别。提出不考虑阻尼的影响,以频率为横坐标,以模态保证准则MAC中任一列数据为纵坐标的模糊聚类算法稳定图。通过比较稳定图中各聚类圆大小的方法进行稳定图真假模态的判别,从而使模态判别不再需要人的主观参与而变得更加智能和准确。通过简支梁和连续梁仿真分析验证了模糊聚类算法稳定图用于桥梁结构参数识别的准确性和较强的抗干扰性。

改进的子空间方法及其在时变结构参数辨识中的应用

本文给出了一种可用于时变结构参数辨识的子空间跟踪方法。子空间方法运用特征分析理论,通过矩阵分解来得到信号子空间。首先将要跟踪的矩阵变换为一种适合在线跟踪的格式,将新的数据信息组合成一个维数不变的矩阵,通过对该矩阵的奇异值分解来更新上一步的信号子空间。这样就避免了对一个不断增长的Hankle阵做奇异值分解,有效的缩减了计算量。将该方法用于机械臂系统,通过施加一个随时间变化的力来改变机械臂的固有频率。选择合适的遗忘因子以协调跟踪能力和辨识仿真结果证实了算法跟踪时变参数的能力。

结构应变模态识别的随机子空间方法

利用应变位移关系,建立了以应变及应变率为状态变量表示的系统随机状态空间模型,在此基础上,比拟实验位移模态分析的随机子空间方法,建立了实验应变模态分析的随机子空间方法。数值仿真表明此算法可以准确辨识结构应变模态。由于此方法仅需要结构输出量,可应用于环境激励下结构应变模态参数识别。

基于随机子空间和最小二乘法的介质损耗角检测方法

介质损耗角是判断高压设备绝缘状况的有效参数之一,而准确地提取基波电压和电流信号是检测介损角的关键。为精确检测介损角,提出基于随机子空间辨识(stochastic subspace identification,SSI)与最小二乘(leastsquare,LS)法的介损角检测方法。该方法可以准确提取基波电压和电流分量,得到基波电压和电流的相位,从而实现介损角的准确检测。当介损角真实值、电压频率、3次谐波、直流分量、采样频率、采样点数以及白噪声等状态变化时,对SSI-LS法基本无影响;当白噪声的信噪比>25 dB时,其计算结果满足介损角的误差精度;经与谐波分析法进行比较分析可知,此算法的速度和精度较优。仿真分析结果验证了该方法的可行性与有效性。