Showing papers on "System identification published in 2002"

Efficient Object Identification with Passive RFID Tags

Abstract: Radio frequency identification systems with passive tags are powerful tools for object identification. However, if multiple tags are to be identified simultaneously, messages from the tags can collide and cancel each other out. Therefore, multiple read cycles have to be performed in order to achieve a high recognition rate. For a typical stochastic anti-collision scheme, we show how to determine the optimal number of read cycles to perform under a given assurance level determining the acceptable rate of missed tags. This yields an efficient procedure for object identification. We also present results on the performance of an implementation.

Model-based fault diagnosis in dynamic systems using identification techniques

Abstract: 1. Introduction.- 2. Model-based Fault Diagnosis Techniques.- 3. System Identification for Fault Diagnosis.- 4. Residual Generation, Fault Diagnosis and Identification.- 5. Fault Diagnosis Application Studies.- 6. Concluding Remarks.- References.

Outliers in process modeling and identification

Abstract: Model-based control strategies like model predictive control (MPC) require models of process dynamics accurate enough that the resulting controllers perform adequately in practice. Often, these models are obtained by fitting convenient model structures (e.g., linear finite impulse response (FIR) models, linear pole-zero models, nonlinear Hammerstein or Wiener models, etc.) to observed input-output data. Real measurement data records frequently contain "outliers" or "anomalous data points," which can badly degrade the results of an otherwise reasonable empirical model identification procedure. This paper considers some real datasets containing outliers, examines the influence of outliers on linear and nonlinear system identification, and discusses the problems of outlier detection and data cleaning. Although no single strategy is universally applicable, the Hampel filter described here is often extremely effective in practice.

Identification Modeling and Characteristics of Miniature Rotorcraft

Abstract: Foreword. Acknowledgements. Nomenclature. Acronyms. 1. Motivation and Background. 2. Frequency Response System Identification. 3. Development of the Identification Model. 4. Identification of the Model. 5. Characteristics of Small-Scale Rotorcraft. 6. Elements of Control Design. 7. Results, Milestones and Future Directions in Aerial Robotics. References. Index.

Derivative Observations in Gaussian Process Models of Dynamic Systems

Abstract: Gaussian processes provide an approach to nonparametric modelling which allows a straightforward combination of function and derivative observations in an empirical model. This is of particular importance in identification of nonlinear dynamic systems from experimental data. 1) It allows us to combine derivative information, and associated uncertainty with normal function observations into the learning and inference process. This derivative information can be in the form of priors specified by an expert or identified from perturbation data close to equilibrium. 2) It allows a seamless fusion of multiple local linear models in a consistent manner, inferring consistent models and ensuring that integrability constraints are met. 3) It improves dramatically the computational efficiency of Gaussian process models for dynamic system identification, by summarising large quantities of near-equilibrium data by a handful of linearisations, reducing the training set size - traditionally a problem for Gaussian process models.

Errata to “Nonlinear Dynamic System Identification Using Chebyshev Functional Link Artificial Neural Networks”

Abstract: A computationally efficient artificial neural network (ANN) for the purpose of dynamic nonlinear system identification is proposed. The major drawback of feedforward neural networks, such as multilayer perceptrons (MLPs) trained with the backpropagation (BP) algorithm, is that they require a large amount of computation for learning. We propose a single-layer functional-link ANN (FLANN) in which the need for a hidden layer is eliminated by expanding the input pattern by Chebyshev polynomials. The novelty of this network is that it requires much less computation than that of a MLP. We have shown its effectiveness in the problem of nonlinear dynamic system identification. In the presence of additive Gaussian noise, the performance of the proposed network is found to be similar or superior to that of a MLP. A performance comparison in terms of computational complexity has also been carried out.

A recurrent fuzzy-neural model for dynamic system identification

Abstract: This paper presents a fuzzy modeling approach for identification of dynamic systems. In particular, a new fuzzy model, the Dynamic Fuzzy Neural Network (DFNN), consisting of recurrent TSK rules, is developed. The premise and defuzzification parts are static while the consequent parts of the fuzzy rules are recurrent neural networks with internal feedback and time delay synapses. The network is trained by means of a novel learning algorithm, named Dynamic-Fuzzy Neural Constrained Optimization Method (D-FUNCOM), based on the concept of constrained optimization. The proposed algorithm is general since it can be applied to locally as well as fully recurrent networks, regardless of their structures. An adaptation mechanism of the maximum parameter change is presented as well. The proposed dynamic model, equipped with the learning algorithm, is applied to several temporal problems, including modeling of a NARMA process and the noise cancellation problem. Performance comparisons are conducted with a series of static and dynamic systems and some existing recurrent fuzzy models. Simulation results show that DFNN compares favorably with its competing rivals and thus it can be considered for efficient system identification.

Using Control Theory to Achieve Service Level Objectives In Performance Management

Abstract: A widely used approach to achieving service level objectives for a software system (eg, an email server) is to add a controller that manipulates the target system’s tuning parameters We describe a methodology for designing such controllers for software systems that builds on classical control theory The classical approach proceeds in two steps: system identification and controller design In system identification, we construct mathematical models of the target system Traditionally, this has been based on a first-principles approach, using detailed knowledge of the target system Such models can be complex and difficult to build, validate, use, and maintain In our methodology, a statistical (ARMA) model is fit to historical measurements of the target being controlled These models are easier to obtain and use and allow us to apply control-theoretic design techniques to a larger class of systems When applied to a Lotus Notes groupware server, we obtain model-fits with R^{2} no lower than 75% and as high as 98% In controller design, an analysis of the models leads to a controller that will achieve the service level objectives We report on an analysis of a closed-loop system using an integral control law with Lotus Notes as the target The objective is to maintain a reference queue length Using root-locus analysis from control theory, we are able to predict the occurrence (or absence) of controller-induced oscillations in the system’s response Such oscillations are undesirable since they increase variability, thereby resulting in a failure to meet the service level objective We implement this controller for a real Lotus Notes system, and observe a remarkable correspondence between the behavior of the real system and the predictions of the analysis This indicates that the control theoretic analysis is sufficient to select controller parameters that meet the desired goals, and the need for simulations is reduced

Prediction Error Estimation Methods

Abstract: This contribution describes a common family of estimation methods for system identification, viz, prediction-error methods. The basic ideas behind these methods are described. An overview of typica ...

Development of adaptive modeling techniques for non-linear hysteretic systems

Abstract: Adaptive estimation procedures have gained significant attention by the research community to perform real-time identification of non-linear hysteretic structural systems under arbitrary dynamic excitations. Such techniques promise to provide real-time, robust tracking of system response as well as the ability to track time variation within the system being modeled. An overview of some of the authors’ previous work in this area is presented, along with a discussion of some of the emerging issues being tackled with regard to this class of problems. The trade-offs between parametric-based modeling and non-parametric modeling of non-linear hysteretic dynamic system behavior are discussed. Particular attention is given to (1) the effects of over- and under-parameterization on parameter convergence and system output tracking performance, (2) identifiability in multi-degree-of-freedom structural systems, (3) trade-offs in setting user-defined parameters for adaptive laws, and (4) the effects of noise on measurement integration. Both simulation and experimental results indicating the performance of the parametric and non-parametric methods are presented and their implications are discussed in the context of adaptive structures and structural health monitoring.

A blind approach to Hammerstein model identification

Abstract: This paper discusses the Hammerstein model identification using a blind approach. By fast sampling at the output, it is shown that identification of the linear part can be achieved based only on the output measurements that makes the Hammerstein model identification possible without knowing the structure of the nonlinearity and the internal variables.

System IDentification Programs for AirCraft (SIDPAC)

Abstract: A collection of computer programs for aircraft system identification is described and demonstrated. The programs, collectively called System IDentification Programs for AirCraft, or SIDPAC, were developed in MATLAB as m-file functions. SIDPAC has been used successfully at NASA Langley Research Center with data from many different flight test programs and wind tunnel experiments. SIDPAC includes routines for experiment design, data conditioning, data compatibility analysis, model structure determination, equation-error and output-error parameter estimation in both the time and frequency domains, real-time and recursive parameter estimation, low order equivalent system identification, estimated parameter error calculation, linear and nonlinear simulation, plotting, and 3-D visualization. An overview of SIDPAC capabilities is provided, along with a demonstration of the use of SIDPAC with real flight test data from the NASA Glenn Twin Otter aircraft. The SIDPAC software is available without charge to U.S. citizens by request to the author, contingent on the requestor completing a NASA software usage agreement.

Numerical Data Fitting in Dynamical Systems

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Analysis of feedback cancellation in hearing aids with Filtered-x LMS and the direct method of closed loop identification

Abstract: Feedback cancellation in hearing aids based on Filtered-X LMS is analyzed. The data used for identification of the feedback path are the output and input signals of the hearing aid. The identification is thus done in a closed loop. Tracking characteristics and bias of the optimal estimate are discussed. The optimal estimate can be biased when the identification is performed in closed loop and the input signal to the hearing aid is not white. It is shown that the bias could be avoided if the spectrum of the input signal was known and the data used to update the internal feedback is prefiltered. The effects of different choices of the design variables of the Filtered-X LMS are discussed. Three alternatives of the fixed filter are evaluated on feedback paths of hearing aids on human subjects and with alternative spectra of the input signal. The optimal estimate of the Filtered-X LMS with an individually adjusted fixed filter shows the best agreement with the desired estimate.

Mechatronic design of a ball-on-plate balancing system

Abstract: This paper discusses the conception and development of a ball-on-plate balancing system based on mechatronic design principles. Realization of the design is achieved with the simultaneous consideration towards constraints like cost, performance, functionality, extendibility, and educational merit. A complete dynamic system investigation for the ball-on-plate system is presented in this paper. This includes hardware design, sensor and actuator selection, system modeling, parameter identification, controller design and experimental testing. The system was designed and built by students as part of the course Mechatronics System Design at Rensselaer.

The generation of binary and near-binary pseudorandom signals: an overview

Abstract: Pseudorandom signals have been widely used for system identification. Maximum length binary (MLB) signals are the best known class of pseudorandom signals, because of their ease of generation using feedback shift registers, but it is less well known that there are several other classes of binary and near-binary signals with identical, or nearly identical properties. An overview of these classes of signals is given, and the design of a new MATLAB routine incorporating all these classes of signals is described. The importance of the choice of MLB signal to use in particular applications is illustrated with the identification of a Wiener system having a quadratic nonlinearity and a cubic nonlinearity. Using correlation analysis to estimate the linear system weighting function, errors due to the nonlinearities can be reduced if an appropriate choice is made of feedback connections and of data length used for the estimation.

Finite sample properties of system identification methods

Abstract: In this paper we study the quality of system identification models obtained using the standard quadratic prediction error criterion for a general linear model class. The main feature of our results is that they hold true for a finite data sample and they are not asymptotic. The main theorems bound the difference between the expected value of the identification criterion evaluated at the estimated parameters and at the optimal parameters. The bound depends naturally on the model and system order, the pole locations, and the noise variance, and it shows that although these variables often do not enter in asymptotic convergence results, they do play an important role when the data sample is finite.