Structural damage identification using system dynamic properties

A damage detection method is presented for the identification and quantification of damage that leads to a change in the structure’s mass and/or stiffness properties. The proposed method requires the use of finite element to model the structure in its undamaged state as well as information on the dynamic properties such as frequencies and mode shapes of the structure in its damaged state. The technique is applicable to any structure that can be accurately modeled using the finite element method and whose frequencies and mode shapes can be reliably measured. A structure pseudo force vector derived from the residual force method is described to locate the damaged regions in the structure. A matrix condensation approach in conjunction with a proportional damage model is then employed to quantify the damage by calculating the change in stiffness and mass properties of the damaged elements in the structure. The validity of the method is demonstrated by applying it to three structures: a beam, a frame and a plate. It is shown that if the amount of damage is not excessively large, the proposed method can be used to detect damage in these structures even when the measured system dynamic properties are slightly erroneous.     

连续系统的模型参考自适应辨识新方法

１引言文［１］提出了将待辨系统以被控对象的形式嵌入到Ｎａｒｅｎｄｒａ等［２］的自适应控制结构中去的连续系统辨识方案,解决了传统的模型参考自适应辨识方法不能处理待辨系统不稳定或不具备良好暂态品质的问题．但它不能处理非最小相位系统,而且对于相对阶大于１并且传递函数分子阶次大于０的系统,因需要进行矩阵求逆而使运算复杂,精度降低．本文在文［１］的基础上,提出了将待辨系统与一个适当选择的相对阶为１的系统并联后作为被控对象嵌入到模型参考自适应控制系统（ＭＲＡＣＳ）中去的辨识方案,解决了上述问题。２辨识方案考…     

Model validation for control and controller validation in a prediction error identification framework—Part II: illustrations

In this paper, we illustrate our new results on model validation for control and controller validation in a prediction error identification framework, developed in a companion paper (Gevers et al., Automatica (2003) 39(3) pii: S005-1098(02)00234-0), through two realistic simulation examples, covering widely different control design applications. The first is the control of a flexible mechanical system (the Landau benchmark example) with a tracking objective, the second is the control of a ferrosilicon production process with a disturbance rejection objective.     

Jackknife and bootstrap methods in the identification of dynamic models

A new criterion based on a Jackknife or a Bootstrap statistic is proposed for identifying non-parsimonious dynamic models (FIR, ARX). It is applicable for selecting the number of components in latent variable regression methods or the constraining parameter in regularized least squares regression methods. These meta parameters are used to overcome ill-conditioning caused by model over-parameterization, when fitted using prediction error or least squares methods. In all cases studied, using PLS for parameter estimation, the proposed criterion led to the selection of better models, in the mean square error sense, than when selected via cross-validation. The methodology also provides approximate confidence intervals for the model parameters and the step and impulse response of the system.     

Adaptive online prediction method based on LS-SVR and its application in an electronic system

Health trend prediction has become an effective way to ensure the safe operation of highly reliable systems,and online prediction is always necessary in many real applications.To simultaneously obtain better or acceptable online prediction accuracy and shorter computing time,we propose a new adaptive online method based on least squares support vector regression(LS-SVR).This method adopts two approaches.One approach is that we delete certain support vectors by judging the linear correlation among the samples to increase the sparseness of the prediction model.This approach can control the loss of useful information in sample data,improve the generalization capability of the prediction model,and reduce the prediction time.The other approach is that we reduce the number of traditional LS-SVR parameters and establish a modified simple prediction model.This approach can reduce the calculation time in the process of adaptive online training.Simulation and a certain electric system application indicate preliminarily that the proposed method is an effective prediction approach for its good prediction accuracy and low computing time.     

Stochastic system transformation using generalized orthonormal basis functions with applications to continuous-time system identification

This paper studies the system transformation using generalized orthonormal basis functions that include the Laguerre basis as a special case. The transformation of the deterministic systems is studied in the literature, which is called the Hambo transform. The aim of the paper is to develop a transformation theory for stochastic systems. The paper establishes the equivalence of continuous and transformed-discrete-time stochastic systems in terms of solutions. The method is applied to the continuous-time system identification problem. It is shown that using the transformed signals the PO-MOESP subspace identification algorithm yields consistent estimates for system matrices. An example is included to illustrate the efficacy of the proposed identification method, and to make a comparison with the method using the Laguerre filter.     

Adaptive stepsizes for recursive estimation with applications in approximate dynamic programming

We address the problem of determining optimal stepsizes for estimating parameters in the context of approximate dynamic programming. The sufficient conditions for convergence of the stepsize rules have been known for 50 years, but practical computational work tends to use formulas with parameters that have to be tuned for specific applications. The problem is that in most applications in dynamic programming, observations for estimating a value function typically come from a data series that can be initially highly transient. The degree of transience affects the choice of stepsize parameters that produce the fastest convergence. In addition, the degree of initial transience can vary widely among the value function parameters for the same dynamic program. This paper reviews the literature on deterministic and stochastic stepsize rules, and derives formulas for optimal stepsizes for minimizing estimation error. This formula assumes certain parameters are known, and an approximation is proposed for the case where the parameters are unknown. Experimental work shows that the approximation provides faster convergence than other popular formulas. Editor: Prasad Tadepalli     

New dynamic fuzzy structure and dynamic system identification

In this study, a new fuzzy system structure that reduces the number of inputs is proposed for dynamic system identification applications. Algebraic fuzzy systems have some disadvantages due to many inputs. As the number of inputs increase, the number of parameters in the training process increase and hence the classical fuzzy system becomes more complex. In the conventional fuzzy system structure, the past information of both inputs and outputs are also regarded as inputs for dynamic systems, therefore the number of inputs may not be manageable even for single input and single output systems. The new dynamic fuzzy system module (DFM) proposed here has only a single input and a single output. We have carried out identification simulations to test the proposed approach and shown that the DFM can successfully identify non-linear dynamic systems and it performs better than the classical fuzzy system.     

Adaptive resource management for dynamic distributed real-time applications

The dynamic distributed real-time applications run on clusters with varying execution time, so re-allocation of resources is critical to meet the applications’s deadline. In this paper we present two adaptive recourse management techniques for dynamic real-time applications by employing the prediction of responses of real-time tasks that operate in time sharing environment and run-time analysis of scheduling policies. Prediction of response time for resource reallocation is accomplished by historical profiling of applications’ resource usage to estimate resource requirements on the target machine and a probabilistic approach is applied for calculating the queuing delay that a process will experience on distributed hosts. Results show that as compared to statistical and worst-case approaches, our technique uses system resource more efficiently.     

Subspace identification using instrumental variable techniques

Subspace-based algorithms for system identification have lately been suggested as alternatives to more traditional techniques. Variants of the MOESP type of subspace algorithms are in addition to open-loop identification applicable to closed-loop and errors-in-variables identification. In this paper, a new instrumental variable approach to subspace identification is presented. It is shown how existing MOESP-algorithms can be derived within the proposed framework, simply by changing instruments and weighting matrices. A noteworthy outcome of the analysis is that an improvement of an existing MOESP method for errors-in-variables identification can be proposed.     

Adaptive filtering of MPEG system streams in IP networks

Congestion and large differences in available link bandwidth create challenges for the design of applications that want to deliver high quality video over the Internet. We present an efficient adaptive filter for MPEG System streams that can be placed in the network (e.g., as an active service). This filter adjusts the bandwidth demands of an MPEG System stream to the available bandwidth without transcoding while maintaining synchronization between the streams embedded in the MPEG System. The filter is network-friendly: it is fair with respect to other (TCP) competing streams and it avoids generating bursty traffic. This paper presents the system architecture and an evaluation of our implementation in three different operating environments: a networking testbed in a laboratory environment, a home-user scenario (DSL line with 640 Kbit/s), and a wide area network covering the Atlantic (server in Europe, client in the US). Moreover we examine the network-friendliness of the adaptation protocol and the relationship between the quality of the received continuous media and the protocol's aggressiveness. Our architecture is based on efficient MPEG System filtering to achieve high-quality video over best-effort networks.Effort sponsored in part by the Advanced Research Projects Agency and Rome Laboratory, Air Force Materiel Command, USAF, under agreement number F30602-96-1-0287. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Advanced Research Projects Agency, Rome Laboratory, or the U.S. Government.     

自适应超媒体技术及其在智能化CAI中的应用

将自适应超媒体的方法和技术应用于智能教学系统,可充分体现因材施教的思想,提高学生的学习效果。文章介绍了自适应超媒体系统的关键方法和技术,描述了智能教学系统的组成与结构,然后结合自适应教学系统KDAES的研制讨论了自适应技术对教学系统智能化的支持及系统核心模块——学生模型的构建。     

数据MIS自适应系统原理及实现

本文提出了一种用户需求关系的描述方法。采用关系库。数据库和模型库与数据处理程序相互独立，实现系统对用户需求的自适应，并在ＦＯＸＢＥＳＥ环境下实现。     

Adaptive input shaping for manoeuvring flexible structures using an algebraic identification technique

Input shaping is an efficient feedforward control technique which has motivated a great number of contributions in recent years. Such a technique generates command signals with which manoeuvre flexible structures without exciting their vibration modes. This paper presents a novel adaptive input shaper based on an algebraic non-asymptotic identification. The main characteristic of the algebraic identification in comparison with other identification methods is the short time needed to obtain the system parameters without defining initial conditions. Thus, the proposed adaptive control can update the input shaper during each manoeuvre when large uncertainties are present. Simulations illustrate the performance of the proposed method.     

面向系统动态可靠性的自适应目标代码生成方法

随着社会的快速发展,人们对嵌入式智能设备的依赖程度越来越高。嵌入式系统工作在体积受限的封闭环境中,运算部件、存储单元等相关元器件体积小集成度高,不同工作环境、不同使用频度对电子元器件的可靠性将产生重要影响。针对嵌入式系统工作过程中的动态可靠性,提出了面向系统动态可靠性的自适应目标代码生成方法。该方法借助于决策树学习算法,构建了系统可靠性评估模型;并以此为指导,设计了多路径目标代码生成方法。使得系统能够根据实际工作状态信息,自适应的最佳的执行路径,以避免系统资源使用的不均衡,提高各运算部件的可靠性。实验表明,该方法将程序对单个处理器最高使用率由80%以上降到了30%以内,将内存单元最大最小访问比例由157.3降到了15.4,有效均衡了各处理器核和内存单元的使用。     

Input design for structured nonlinear system identification

This paper is concerned with the input design problem for a class of structured nonlinear models. This class contains models described by an interconnection of known linear dynamic systems and unknown static nonlinearities. Many widely used model structures are included in this class. The model class considered naturally accommodates a priori knowledge in terms of signal interconnections. Under certain structural conditions, the identification problem for this model class reduces to standard least squares. We treat the input design problem in this situation.An expression for the expected estimate variance is derived. A method for synthesizing an informative input sequence that minimizes an upper bound on this variance is developed. This reduces to a convex optimization problem. Features of the solution include parameterization of the expected estimate variance by the input distribution, and a graph-based method for input generation.     

天线伺服系统的快速辨识算法

天线伺服控制系统在移动卫星通讯中起着关键性作用,其对象模型的品质直接影响天线伺服控制系统的设计与性能.本文针对天线伺服系统的建模问题提出一种基于系统阶跃响应的快速优化建模方法.利用这一结果,本文建立了0.9 m小口径天线伺服系统的二阶模型.进一步,讨论了基于系统伪随机响应谱密度函数的模型有效性验证方法和模型品质的评价准则,并分别在频域和时域中将由本文方法及最小二乘法所得的模型与系统实际数据进行对比分析.本文辨识方法的优点是计算简单,易于工程应用,对系统阶次不敏感并且能较为精确地描述系统主谐振峰的特性,适用于实际工程中的参数在线整定.     

Subspace-based system identification: weighting and pre-filtering of instruments

Subspace-based system identification is typically based on an estimate of the extended observability matrix. It is thus of great interest to investigate, and also optimize, the estimate of the observability matrix. Of special interest in this paper is the fact that the influence of certain weighting matrices is an unresolved issue in the literature on subspace identification. Here, an asymptotic analysis of the estimated observability matrix is presented. The main result of the analysis is that novel weighting matrices and pre-filters of instrumental variables are derived.     

Adaptive control of a hysteretic structural system

This paper addresses the problem of controlling a second-order uncertain structural hysteretic system. The hysteretic behavior is represented by a Bouc-Wen model whose parameters lie within intervals. It is shown that the nonlinear response of the model acts as a bounded disturbance. This fact is used to elaborate an adaptive controller, to prove the closed-loop stability and to obtain performance bounds.