基于广义子空间溯踪和强震观测的混凝土坝时变模态参数识别方法研究

混凝土坝在地震过程中可能会出现时变特性。本文将地震激励下结构的时变模态识别问题表述为一个子空间溯踪问题,提出了采用广义子空间溯踪算法结合递归随机子空间识别方法来进行混凝土坝时变模态识别;结合数值算例,验证了该方法的识别精度、鲁棒性和计算效率;最后基于某混凝土重力坝的强震观测数据,采用本文提出的GYAST-RSSI时变模态识别方法,追踪地震中混凝土坝模态参数变化,分析混凝土坝在地震中的时变特征。结果表明,该方法对频率、振型的识别精度较好,且方法鲁棒性强,计算效率高。     

多输入—多输出实验模态参数识别（2）：时域识别法及其内在联系

本文在多输入——多输出实验模态参数识别技术离散统一模型的基础上,介绍了几种得到广泛应用的多输入——多输出实验模态参数时域识别技术,如直接参数模型识别法,Polyreference 识别法,Ibrahim 时域法和特征系统实现算法(ERA),并将这几种算法统一在一个基本的数学模型—离散的统一模型上,指出了这几种识别方法相互之间的联系。     

结构动力学中传递函数与模态参数识别

本文从任意粘性阻尼离散化线性结构系统的复模态理论出发,建立了结构系统传递函数与模态参数的一般解析关系式.在最优化方法基础上提出了两种传递函数和模态参数识别方法.用计算机试验、检验了两种识别算法的精度和使用范围.给出了真实结构识别全过程的实例.     

基于模态数据的长细结构小损伤精确识别研究

本文针对现有的损伤识别方法不能满足部分结构损伤识别精度要求的现状，对结构的小损伤精确识别方法开展研究．以长细结构为研究对象，对具有不同损伤位置和损伤程度的圆柱形的轻阻尼梁结构进行了数值分析和实验研究，应用数值计算方法和实验确定的特征向量和特征频率对长细结构裂缝参数进行识别计算．本文在研究过程中编制了一个创新性的预测程序，通过其一次性生成目标函数图来选择合适的初始参数，从而对识别结果进行分析．研究结果表明，应用本文提出的识别方法，裂缝位置的识别误差可以控制在0.05 %~0.28 %范围内，裂缝深度识别误差低于7 %．     

汽(气)液两相流流型在线识别的研究进展

综述了根据参数波动过程实现气液两相流流型在线识别的最新 研究成果,内容包括两相流参数波动的产生机理,小波分析的应用, 两相流参数波动过程的特征提取和特征分析,流型在线识别的特点及 各种实现方法等。重点介绍了两相流参数波动过程的统计和非线性特 征分析及其与流型之间的关系。深入讨论了流型神经网络识别方法及 其存在的问题。从波动参数的选择、数理解释、流型识别方法等不同 方面对研究进展进行了讨论。     

对随机减量技术的数学表示

自1973年Cole提出随机减量技术,1977年Ibrahim加以发展推广,之后,在振动模态参数识别方面得到了成功的应用.本文推导了随机减量特征信号的数学表达式,这个表达式表明,随机减量特征信号和方差函数是完全等价的.本文计算了一个三自由度系统的例子来验证所导出的结论.还介绍了在振动参数识别中使用随机减量技术和方差函数的理论基础.对在不同场合使用的一些时域识别方法之间的关系给了证明.     

基于广义卡尔曼滤波的桥梁结构物理参数识别

基于广义卡尔曼滤波提出了随机荷载作用下桥梁结构物理参数的识别方法。首先,以荷载为观测对象,推导出基于有限元模型的桥梁结构系统的观测方程,以结构待识别的物理参数为状态向量,建立系统状态方程;然后,对该状态方程和观测方程构成的非线性参数系统应用广义卡尔曼滤波,从而识别出结构的物理参数。对一座简支梁桥和一座三跨连续梁桥在不同工况下的物理参数识别进行了数值仿真,结果表明本文方法能够准确地识别桥梁结构全部刚度参数、质量参数和阻尼参数,且具有很强的抗噪性能,从而验证了本文方法的有效性和鲁棒性,可应用于识别大型桥梁结构的物理参数。     

高温下编织复合材料热相关参数识别方法研究

为了获取高温下编织复合材料的准确弹性参数与热膨胀系数,提出一种基于均匀化理论的热相关参数识别方法. 首先,在编织复合材料单胞有限元模型基础上,基于均匀化理论和热弹性理论,施加周期性位移边界条件和温度边界条件,预测编织复合材 料的热弹性相关参数. 然后,考虑到等效过程中编织复合材料应力分布不均匀等因素引起的误差,将复合材料精细模型的热模态数据作为补 充信息,识别编织复合材料热相关参数,对预测的材料参数进行校准. 本文在二维编织结构单胞模型基础上,开展等效预测和识别方法研 究,验证所提出方法的有效性和准确性. 对比等效和识别后热模态的误差,结果表明:本文提出的基于等效预测的参数识别方法,能够 准确识别高温下编织复合材料宏观热相关参数.      

复合材料胶接修理损伤金属结构的研究现状

损伤金属飞机结构的复合材料补片胶接修理技术是一项经济而有效的结构延寿方法．详细 叙述了这种修理技术的优缺点及其技术要点,涉及的内容包括胶粘剂的选择、补片材料和参数设计、 表面处理、无损检测和试验验证等．介绍了国内外对该项先进修理技术的研究现状和在飞机结构上 的实际使用情况．评述了现有的理论分析和试验研究方法及其结果．     

高温下编织复合材料热相关参数识别方法研究

为了获取高温下编织复合材料的准确弹性参数与热膨胀系数,提出一种基于均匀化理论的热相关参数识别方法.首先,在编织复合材料单胞有限元模型基础上,基于均匀化理论和热弹性理论,施加周期性位移边界条件和温度边界条件,预测编织复合材料的热弹性相关参数.然后,考虑到等效过程中编织复合材料应力分布不均匀等因素引起的误差,将复合材料精细模型的热模态数据作为补充信息,识别编织复合材料热相关参数,对预测的材料参数进行校准.本文在二维编织结构单胞模型基础上,开展等效预测和识别方法研究,验证所提出方法的有效性和准确性.对比等效和识别后热模态的误差,结果表明:本文提出的基于等效预测的参数识别方法,能够准确识别高温下编织复合材料宏观热相关参数.     

Identification of time-varying hysteretic structures using wavelet multiresolution analysis

In this paper, a wavelet multiresolution technique is proposed to identify time-varying properties of hysteretic structures. It is well known that arbitrary transient functions can be effectively and accurately approximated using wavelet multiresolution expansions due to wavelet's good time-frequency localization property. By decomposing the time-varying parameters with wavelet multiresolution expansion, a time-varying parametric identification problem can be transformed into a time-invariant non-parametric one. The identification in the time-invariant wavelet multiresolution domain can be achieved by choosing a wavelet basis function and performing a suitable parameter estimation technique. Since wavelet representation of arbitrary signal uses only a small number of terms, the orthogonal forward regression algorithm can be adopted for significant term selection and parameter estimation. Single and multiple degrees of freedom Bouc-Wen hysteretic structures with gradual and abrupt varying properties are used to illustrate the proposed approach. Results show that the wavelet multiresolution technique can identify and track the time-varying hysteretic parameters quite accurately. The effect of measurement noise is also studied. It is found that the presence of noise would affect more on the damping ratios and the Bouc-Wen parameters but less on the equivalent stiffness coefficients.     

Disturbance rejection-based robust control for micropositioning of piezoelectric actuators

Precise control of piezoelectric actuators used in micropositioning applications is strongly under the effect of internal and external disturbances. Undesired external forces, unmodelled dynamics, parameter uncertainties, time variation of parameters and hysteresis are some sources of disturbances. These effects not only degrade the performance efficiency, but also may lead to closed-loop instability. Several works have investigated the positioning accuracy for constant and slow time-varying disturbances. The main concern is controlling performance and also the presence of time-varying perturbations. Considering unknown source and magnitude of disturbances, the estimation of the existing disturbances would be inevitable. In this paper, a compound disturbance observer-based robust control is developed to achieve precise positioning in the presence of time-varying disturbances. In addition, a modified disturbance observer is proposed to remedy the effect of switching behaviour in the case of slow time variations. A modified Prandtl–Ishlinskii (PI) operator and its inverse are utilized for both identification and real-time compensation of the hysteresis effect. Experimental results depict that the proposed approach achieves precise micropositioning in the presence of estimated disturbances.     

Hydrodynamics of a flexible cylinder under modulated vortex-induced vibrations

Both amplitude modulation and frequency modulation of Vortex-induced Vibration (VIV) are observed in a recent model test of a flexible cylinder under oscillatory flow, but its hydrodynamics has not yet been broached in detail. This paper employs the Forgetting Factor Least Squares (FF-LS) method for identification of time-varying hydrodynamics of a flexible cylinder under modulated VIV. The FF-LS method’s applicability to accurately identify time-varying hydrodynamic coefficients is demonstrated through an elastically mounted rigid cylinder under flow with a given modulated motion. Furthermore, we propose a framework to predict instantaneous amplitude (envelope) and frequency using time-varying hydrodynamic coefficients to establish their analytical relationship. This prediction method is further extended to a highly tensioned flexible cylinder through Fourier series expansion in the spatial domain. By performing the identification procedure for all sampled data of a flexible cylinder undergoing oscillatory flow, we obtain the corresponding time-varying hydrodynamics in the cross-flow direction considering the amplitude and frequency modulation. The results show that, under modulated VIV, hydrodynamic coefficients of the flexible cylinder also show time-varying characteristics. We further investigate differences between identified hydrodynamic coefficients and those obtained from the database of a cylinder with modulated motion under flow. Prediction results using these identified time-varying coefficients reveal that the time-varying excitation coefficients mainly influence the amplitude modulation, and the time-varying added-mass coefficients contain the major information of frequency modulation. These results further suggest including the temporal derivative of the instantaneous amplitude as one determining parameter in building databases to improve the prediction of modulated VIV.     

Robust synchronization of uncertain fractional-order chaotic systems with time-varying delay

This paper presents a new technique using a recurrent non-singleton type-2 sequential fuzzy neural network (RNT2SFNN) for synchronization of the fractional-order chaotic systems with time-varying delay and uncertain dynamics. The consequent parameters of the proposed RNT2SFNN are learned based on the Lyapunov–Krasovskii stability analysis. The proposed control method is used to synchronize two non-identical and identical fractional-order chaotic systems, with time-varying delay. Also, to demonstrate the performance of the proposed control method, in the other practical applications, the proposed controller is applied to synchronize the master–slave bilateral teleoperation problem with time-varying delay. Simulation results show that the proposed control scenario results in good performance in the presence of external disturbance, unknown functions in the dynamics of the system and also time-varying delay in the control signal and the dynamics of system. Finally, the effectiveness of proposed RNT2SFNN is verified by a nonlinear identification problem and its performance is compared with other well-known neural networks.     

Parameter identifications for a rotor system based on its finite element model and with varying speeds

In order to achieve prediction for vibration of rotating machinery, an accurate finite element （FE） model and an efficient parameter identification method of the rotor system are required. In this research, a test rig is used as a prototype of a rotor system to validate a novel parameter identification technique based on an FE model. Rotor shaft vibration at varying operating speeds is measured and correlated with the FE results. Firstly, the theories of the FE modelling and identification technique are introduced. Then disk unbalance parameter, stiffness and damping coefficients of the bearing supports on the test rig are identified. The calculated responses of the FE model with identified parameters are studied in comparison with the experimental results.     

A modified time domain subspace method for nonlinear identification based on nonlinear separation strategy

Nonlinear factors existing in engineering structures have drawn considerable attention, and nonlinear identification is a competent technique to understand the dynamic characteristics of nonlinear structures. Therefore, in this paper, a novel nonlinear separation subspace identification (NSSI) algorithm based on subspace algorithm and nonlinear separation strategy is proposed to conduct nonlinear parameter identification of nonlinear structures. For the proposed NSSI algorithm, the low-level excitation test is firstly conducted to obtain the transfer matrix in the linear response formula. Then, the obtained transfer matrix is used in the high-level excitation test to calculate the nonlinear response part by the proposed nonlinear separation strategy, and the subspace algorithm is utilized to identify the nonlinear parameter on the modified state-space model including only the nonlinear part. The proposed NSSI algorithm can reduce the coupling error caused by simultaneously processing both the large number part (corresponding to the linear part) and small number part (corresponding to the nonlinear part) in the traditional nonlinear subspace identification (NSI) algorithm. At last, two numerical experiments are given to validate the effectiveness of the developed novel nonlinear identification method. Furthermore, some influence factors are discussed to show the stability of the identification algorithm, and some comparisons between the proposed NSSI method and traditional NSI method are also conducted to demonstrate the advantages of the novel method.     

Design of state estimator for uncertain neural networks via the integral-inequality method

The issue of state estimation is studied for a class of neural networks with norm-bounded parameter uncertainties and time-varying delay. Some new linear matrix inequality (LMI) representations of delay-dependent stability criteria are presented for the existence of the desired estimator for all admissible parametric uncertainties. The proposed method is based on the S-procedure and an extended integral inequality which can be deduced from the well-known Leibniz–Newton formula and Moon’s inequality. The results extend some models reported in the literature and improve conservativeness of those in the case that the derivative of the time-varying delay is assumed to be less than one. Two numerical examples are given to show the effectiveness and superiority of the results.     

TIME DOMAIN PARAMETERS IDENTIFICATION OF FOUNDATION-STRUCTURE INTERACTION SYSTEM

The time domain parameter laenuncauon memoa oi me iounuauon-structure interaction system is presented. On the basis of building the computation mode and the motion equation of the foundation-structure interaction system, the system parameter identification method was established by using the extended Kalman filter （EKF） technique and taking the unknown parameters in the system as the augment state variables. And the time parameter identification process of the foundation-structure interaction system was implemented by using the data of the layer foundation-storehouse interaction system model test on the large vibration platform. The computation result shows that the established parameter identification method can induce good parameter estimation.     

基于混合遗传算法的动力系统阻尼参数识别方法

将动力系统阻尼参数识别反问题转化为非线性优化问题处理，提出了基于遗传算法的动力系统阻尼参数识别方法。为了提高简单遗传算法的计算效率和处理早熟问题，将模拟退火算法与遗传算法相结合，建立了混合遗传算法。数值计算结果表明，本文所建立的方法对于求解参数识别反问题和非线性优化问题是非常有效的，并且具有良好的鲁棒性和全局收敛能力。     

Parameter identification of bidirectional IPT system using chaotic asexual reproduction optimization

Bidirectional inductive power transfer (IPT) system facilitates contactless power transfer between two sides and across an air gap, through weak magnetic coupling. Typically, this system constitutes a high-order resonant circuit and, as such, is difficult to design and control. In this study, a novel technique for parameter identification of bidirectional IPT system is presented by using chaotic asexual reproduction optimization (CARO). The asexual reproduction optimization (ARO) is a novel kind of evolutionary-based algorithm that mathematically models the budding mechanism of asexual reproduction. The CARO employs chaotic sequence to enhance ARO’s global searching ability. The parameter identification of a bidirectional IPT system is posed as an optimization process with an objective function minimizing the errors between the estimated and measured value. The implementation of the CARO-based parameter identification technique is analyzed in detail. Simulations are used to test the robustness and generalization ability of the proposed technique.