Showing papers on "System identification published in 2008"

Overview of Identification Methods of Mechanical Parameters Based on Full-field Measurements

Abstract: This article reviews recently developed methods for constitutive parameter identification based on kinematic full-field measurements, namely the finite element model updating method (FEMU), the constitutive equation gap method (CEGM), the virtual fields method (VFM), the equilibrium gap method (EGM) and the reciprocity gap method (RGM) Their formulation and underlying principles are presented and discussed These identification techniques are then applied to full-field experimental data obtained on four different experiments, namely (i) a tensile test, (ii) the Brazilian test, (iii) a shear-flexural test, and (iv) a biaxial test Test (iv) features a non-uniform damage field, and hence non-uniform equivalent elastic properties, while tests (i), (ii) and (iii) deal with the identification of uniform anisotropic elastic properties Tests (ii), (iii) and (iv) involve non-uniform strain fields in the region of interest

Homogeneous Approximation, Recursive Observer Design, and Output Feedback

Abstract: We introduce two new tools that can be useful in nonlinear observer and output feedback design. The first one is a simple extension of the notion of homogeneous approximation to make it valid both at the origin and at infinity (homogeneity in the bi-limit). Exploiting this extension, we give several results concerning stability and robustness for a homogeneous in the bi-limit vector field. The second tool is a new recursive observer design procedure for a chain of integrator. Combining these two tools, we propose a new global asymptotic stabilization result by output feedback for feedback and feedforward systems.

Optimal Estimation in Networked Control Systems Subject to Random Delay and Packet Drop

Abstract: In this note, we study optimal estimation design for sampled linear systems where the sensors measurements are transmitted to the estimator site via a generic digital communication network. Sensor measurements are subject to random delay or might even be completely lost. We show that the minimum error covariance estimator is time-varying, stochastic, and it does not converge to a steady state. Moreover, the architecture of this estimator is independent of the communication protocol and can be implemented using a finite memory buffer if the delivered packets have a finite maximum delay. We also present two alternative estimator architectures that are more computationally efficient and provide upper and lower bounds for the performance of the time-varying estimator. The stability of these estimators does not depend on packet delay but only on the overall packet loss probability. Finally, algorithms to compute critical packet loss probability and estimators performance in terms of their error covariance are given and applied to some numerical examples.

Macromodeling of Multiport Systems Using a Fast Implementation of the Vector Fitting Method

Abstract: Broadband macromodeling of large multiport systems by vector fitting can be time consuming and resource demanding when all elements of the system matrix share a common set of poles. This letter presents a robust approach which removes the sparsity of the block-structured least-squares equations by a direct application of the QR decomposition. A 60-port printed circuit board example illustrates that considerable savings in terms of computation time and memory requirements are obtained.

Identification of Continuous-time Models from Sampled Data

Abstract: System identification is an established field in the area of system analysis and control. It aims to determine particular models for dynamical systems based on observed inputs and outputs. Although dynamical systems in the physical world are naturally described in the continuous-time domain, most system identification schemes have been based on discrete-time models without concern for the merits of natural continuous-time model descriptions. The continuous-time nature of physical laws, the persistent popularity of predominantly continuous-time proportional-integral-derivative control and the more direct nature of continuous-time fault diagnosis methods make continuous-time modeling of ongoing importance. Identification of Continuous-time Models from Sampled Data brings together contributions from well-known experts who present an up-to-date view of this active area of research and describe recent methods and software tools developed in this field. They offer a fresh look at and new results in areas such as: time and frequency domain optimal statistical approaches to identification; parametric identification for linear, nonlinear and stochastic systems; identification using instrumental variable, subspace and data compression methods; closed-loop and robust identification; and continuous-time modeling from non-uniformly sampled data and for systems with delay. The CONtinuous-Time System IDentification (CONTSID) toolbox described in the book gives an overview of developments and practical examples in which MATLAB can be brought to bear in the cause of direct time-domain identification of continuous-time systems.This survey of methods and results in continuous-time system identification will be a valuable reference for a broad audience drawn from researchers and graduate students in signal processing as well as in systems and control. It also covers comprehensive material suitable for specialised graduate courses in these areas.

A Robust Variable Forgetting Factor Recursive Least-Squares Algorithm for System Identification

Abstract: The performance of the recursive least-squares (RLS) algorithm is governed by the forgetting factor. This parameter leads to a compromise between (1) the tracking capabilities and (2) the misadjustment and stability. In this letter, a variable forgetting factor RLS (VFF-RLS) algorithm is proposed for system identification. In general, the output of the unknown system is corrupted by a noise-like signal. This signal should be recovered in the error signal of the adaptive filter after this one converges to the true solution. This condition is used to control the value of the forgetting factor. The simulation results indicate the good performance and the robustness of the proposed algorithm.

FLEXFIS: A Robust Incremental Learning Approach for Evolving Takagi–Sugeno Fuzzy Models

Abstract: In this paper, we introduce a new algorithm for incremental learning of a specific form of Takagi-Sugeno fuzzy systems proposed by Wang and Mendel in 1992. The new data-driven online learning approach includes not only the adaptation of linear parameters appearing in the rule consequents, but also the incremental learning of premise parameters appearing in the membership functions (fuzzy sets), together with a rule learning strategy in sample mode. A modified version of vector quantization is exploited for rule evolution and an incremental learning of the rules' premise parts. The modifications include an automatic generation of new clusters based on the nature, distribution, and quality of new data and an alternative strategy for selecting the winning cluster (rule) in each incremental learning step. Antecedent and consequent learning are connected in a stable manner, meaning that a convergence toward the optimal parameter set in the least-squares sense can be achieved. An evaluation and a comparison to conventional batch methods based on static and dynamic process models are presented for high-dimensional data recorded at engine test benches and at rolling mills. For the latter, the obtained data-driven fuzzy models are even compared with an analytical physical model. Furthermore, a comparison with other evolving fuzzy systems approaches is carried out based on nonlinear dynamic system identification tasks and a three-input nonlinear function approximation example.

Robust Linear Output Feedback Control of an Airbreathing Hypersonic Vehicle

Abstract: This paper addresses issues related to robust output-feedback control for a model of an airbreathing hypersonic vehicle. The control objective is to provide robust velocity and altitude tracking in the presence of model uncertainties and varying flight conditions, using only limited state information. A baseline control design based on a robust full-order observer is shown to provide, in nonlinear simulations, insufficient robustness with respect to variations of the vehicle dynamics due to fuel consumption. An alternative approach to robust output-feedback design, which does not employ state estimation, is presented and shown to provide an increased level of performance. The proposed methodology reposes upon robust servomechanism theory and makes use of a novel internal model design. Robust compensation of the unstable zero dynamics of the plant is achieved by using measurements of pitch rate. The selection of the plant's output map by sensor placement is an integral part of the control design procedures, accomplished by preserving certain system structures that are favorable for robust control design. The performance of each controller is comparatively evaluated by means of simulations of a full nonlinear model of the vehicle dynamics and is tested on a given range of operating conditions.

Nonintrusive Load Monitoring and Diagnostics in Power Systems

Abstract: This paper describes a transient event classification scheme, system identification techniques, and implementation for use in nonintrusive load monitoring. Together, these techniques form a system that can determine the operating schedule and find parameters of physical models of loads that are connected to an AC or DC power distribution system. The monitoring system requires only off-the-shelf hardware and recognizes individual transients by disaggregating the signal from a minimal number of sensors that are installed at a central location in the distribution system. Implementation details and field tests for AC and DC systems are presented.

Open-Loop Digital Predistorter for RF Power Amplifiers Using Dynamic Deviation Reduction-Based Volterra Series

Abstract: In this paper, we propose an efficient open-loop digital predistorter (DPD) derived from the dynamic deviation reduction-based Volterra series that allows compensation for both nonlinear distortion and memory effects induced by RF power amplifiers in wireless transmitters. In this approach, the parameters of the predistorter can be directly extracted from an offline system identification process. This eliminates the usual requirement for a closed-loop real-time parameter adaptation, which dramatically reduces the implementation complexity of the system. It is shown that a further reduction in system complexity can be achieved by applying under-sampling theory in the model extraction and utilizing parameter interpolation in the DPD implementation. Experimental results show that by utilizing this technique with only a small number of parameters, nonlinear distortion induced by the PA can be significantly reduced, as evaluated by both adjacent channel power ratio reduction and normalized root mean square error improvement. A comparison with a memoryless polynomial function based predistorter and an analysis of the impact of decresting are also presented.

Structural equation models

Abstract: Structural equation models refer to general statistical procedures for multiequation systems that include continuous latent variables, multiple indicators of concepts, errors of measurement, errors in equations, and observed variables. An analysis that uses structural equation models has several components. These include (a) model specification, (b) the implied moment matrix, (c) identification, (d) estimation, (e) model fit, and (f) respecification. Historical origins of structural equation models are also described. Keywords: structural equation models; factor loading matrix; path analysis; implied moment matrix; model identification; respecification

Data-driven simulation and control

Abstract: Classical linear time-invariant system simulation methods are based on a transfer function, impulse response, or input/state/output representation. We present a method for computing the response of a system to a given input and initial conditions directly from a trajectory of the system, without explicitly identifying the system from the data. Similarly to the classical approach for simulation, the classical approach for control is model-based: first a model representation is derived from given data of the plant and then a control law is synthesized using the model and the control specifications. We present an approach for computing a linear quadratic tracking control signal that circumvents the identification step. The results are derived assuming exact data and the simulated response or control input is constructed off-line.

Bayesian Updating and Model Class Selection for Hysteretic Structural Models Using Stochastic Simulation

Abstract: System identification of structures using their measured earthquake response can play a key role in structural health monitoring, structural control and improving performance-based design. Implementation using data from strong seismic shaking is complicated by the nonlinear hysteretic response of structures. Furthermore, this inverse problem is ill-conditioned for example, even if some components in the structure show substantial yielding, others will exhibit nearly elastic response, producing no information about their yielding behavior. Classical least-squares or maximum likelihood estimation will not work with a realistic class of hysteretic models because it will be unidentifiable based on the data. It is shown here that Bayesian updating and model class selection provide a powerful and rigorous approach to tackle this problem when implemented using a recently developed stochastic simulation algorithm called Transitional Markov Chain Monte Carlo. The updating and model class selection is performed on a previously-developed class of Masing hysteretic structural models that are relatively simple yet can give realistic responses to seismic loading. The theory for the Masing hysteretic models, and the theory used to perform the updating and model class selection, are presented and discussed. An illustrative example is given that uses simulated dynamic response data and shows the ability of the algorithm to identify hysteretic systems even when the class of models is unidentifiable based on the data.

Model selection approaches for non-linear system identification: a review

Abstract: The identification of non-linear systems using only observed finite datasets has become a mature research area over the last two decades. A class of linear-in-the-parameter models with universal approximation capabilities have been intensively studied and widely used due to the availability of many linear-learning algorithms and their inherent convergence conditions. This article presents a systematic overview of basic research on model selection approaches for linear-in-the-parameter models. One of the fundamental problems in non-linear system identification is to find the minimal model with the best model generalisation performance from observational data only. The important concepts in achieving good model generalisation used in various non-linear system-identification algorithms are first reviewed, including Bayesian parameter regularisation and models selective criteria based on the cross validation and experimental design. A significant advance in machine learning has been the development of the support vector machine as a means for identifying kernel models based on the structural risk minimisation principle. The developments on the convex optimisation-based model construction algorithms including the support vector regression algorithms are outlined. Input selection algorithms and on-line system identification algorithms are also included in this review. Finally, some industrial applications of non-linear models are discussed.

Dynamic Modeling, Predictive Control and Performance Monitoring: A Data-driven Subspace Approach

Abstract: I Dynamic Modeling through Subspace Identification.- System Identification: Conventional Approach.- Open-loop Subspace Identification.- Closed-loop Subspace Identification.- Identification of Dynamic Matrix and Noise Model Using Closed-loop Data.- II Predictive Control.- Model Predictive Control: Conventional Approach.- Data-driven Subspace Approach to Predictive Control.- III Control Performance Monitoring.- Control Loop Performance Assessment: Conventional Approach.- State-of-the-art MPC Performance Monitoring.- Subspace Approach to MIMO Feedback Control Performance Assessment.- Prediction Error Approach to Feedback Control Performance Assessment.- Performance Assessment with LQG-benchmark from Closed-loop Data.

A review of nonlinear dynamics applications to structural health monitoring

Abstract: The process of implementing a damage detection strategy for aerospace, civil and mechanical engineering infrastructure is referred to as structural health monitoring (SHM). In many cases damage causes a structure that initially behaves in a predominantly linear manner to exhibit nonlinear response when subject to its operating environment. The formation of cracks that subsequently open and close under operating loads is an example of such damage. The damage detection process can be significantly enhanced if one takes advantage of these nonlinear effects when extracting damage-sensitive features from measured data. This paper will provide a review of examples from nonlinear dynamical systems theory and from nonlinear system identification techniques that are used for the feature-extraction portion of the damage detection process. This paper is not intended as a comprehensive review of all damage detection methods rooted in nonlinear dynamics, but rather to provide a number of illustrations of complimentary approaches where damage-sensitive data features are based on nonlinear system response. These features, in turn, can either be used as a direct diagnosis of damage or as input to statistical damage classifier. Copyright © 2007 John Wiley & Sons, Ltd.

A New Robust Variable Step-Size NLMS Algorithm

Abstract: A new framework for designing robust adaptive filters is introduced. It is based on the optimization of a certain cost function subject to a time-dependent constraint on the norm of the filter update. Particularly, we present a robust variable step-size NLMS algorithm which optimizes the square of the a posteriori error. We also show the link between the proposed algorithm and another one derived using a robust statistics approach. In addition, a theoretical model for predicting the transient and steady-state behavior and a proof of almost sure filter convergence are provided. The algorithm is then tested in different environments for system identification and acoustic echo cancelation applications.