System Identification: A Frequency Domain Approach

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Especially for these readers, we have added guidelines to many chapters for the user, giving explicit and clear advice on what are good choices in order to attain a sound solution. Another important part of the material is intended for readers who want to study identification techniques at a more profound level.

Questions on how to analyze and prove the properties of an identification scheme are addressed in this part. This study is not restricted to the identification of linear dynamic systems; it is valid for a very wide class of weighted, nonlinear least squares estimators. As such, this book provides a great deal of information for readers who want to set up their own identification scheme to solve their specific problem.

The structure of the book can be split into four parts: 1 collection of raw data or nonparametric identification; 2 parametric identification; 3 comparison with existing frameworks, guidelines, and illustrations; 4 profound development of theoretical tools. In the first part, after the introductory chapter on identification, we discuss the collection of the raw data: How to measure a frequency response function of a system.

What is the impact of nonlinear distortions?

A frequency domain approach for parameter identification in multibody dynamics

How to recognize, qualify, and quantify nonlinear distortions. How to select the excitation signals in order to get the best measurements.

This nonparametric approach to identification is discussed in detail in Chapters 2, 3, and 4. In the second part, we focus on the identification of parametric models. Signal and system models are presented, using a frequency and a time domain representation. The equivalence and impact of leakage effects and initial conditions are shown. Nonparametric and parametric noise models are introduced.

The estimation of the parameters in these models is studied in detail.

Weighted nonlinear least squares methods, maximum likelihood, and subspace methods are discussed and analyzed. First, we assume that the disturbing noise model is known; next, the methods are extended to the more realistic situation of unknown noise models that have to be extracted from the data, together with the system model. Special attention is paid to the numerical conditioning of the sets of equations, to be solved.

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Taking some precautions, very high order systems, with poles and zeros or even more, can be identified. Finally, validation tools to verify the quality of the models are explained. The presence of unmodeled dynamics or nonlinear distortions is detected, and simple rules to guide even the inexperienced user to a good solution are given.

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This material is presented in Chapters 5 to 9. The third part begins with an extensive comparison of what is classically called time and frequency domain identi fication. It is shown that, basically, both approaches are equivalent, but some questions are more naturally answered in one domain instead of the other. The most important question is periodic excitations versus nonperiodic or arbitrary excitations.

Next, we provide the practitioner with detailed guidelines to help avoid pitfalls from the very begin- ning of the process collecting the raw data , over the selection of appropriate identification methods until the model validation.

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Finally, we illustrate many of the developed ideas in a wide variety of examples from different fields. This part covers Chapters 10, 11, and To include a comma in your tag, surround the tag with double quotes. Please enable cookies in your browser to get the full Trove experience. Skip to content Skip to search.

Pintelon, R.

Language English View all editions Prev Next edition 8 of 8. Author Pintelon, R. Rik Other Authors Schoukens, J. Physical Description xxxviii, p. Subjects System identification. Contents Machine derived contents note: Preface. List of Operators and National Conventions. List of Symbols.

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List of Abbreviations. An Introduction to Identification. Measurements of Frequency Response Functions. Design of Excitation Signals.

System identification

Models of Linear Time-Invariant Systems. Estimation with Known Noise Model. Estimation with Unknown Noise Model. Model Selection and Validation. Base Choices in System Identification.

System Identification A Frequency Domain Approach Key Features

April Advances in Dental Research. July Modeling MR-dampers: a nonlinear blackbox approach. Proceedings of the American Control Conference. European Journal of Control. Outline Index. Descriptive statistics. Mean arithmetic geometric harmonic Median Mode. Central limit theorem Moments Skewness Kurtosis L-moments. Index of dispersion. Grouped data Frequency distribution Contingency table. Pearson product-moment correlation Rank correlation Spearman's rho Kendall's tau Partial correlation Scatter plot.

Data collection. Sampling stratified cluster Standard error Opinion poll Questionnaire. Scientific control Randomized experiment Randomized controlled trial Random assignment Blocking Interaction Factorial experiment. Adaptive clinical trial Up-and-Down Designs Stochastic approximation.

Cross-sectional study Cohort study Natural experiment Quasi-experiment. Statistical inference. Z -test normal Student's t -test F -test. Bayesian probability prior posterior Credible interval Bayes factor Bayesian estimator Maximum posterior estimator. Correlation Regression analysis. Pearson product-moment Partial correlation Confounding variable Coefficient of determination.