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Frequency Domain System Identification
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Policy Office Website. Jitendra Tugnait tugnajk eng. System identification is a general term used to describe mathematical tools and algorithms that build dynamical models from measured data. Used for prediction, control, physical interpretation, and the designing of any electrical systems, they are vital in the fields of electrical, mechanical, civil, and chemical engineering.
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Focusing mainly on frequency domain techniques, System Identification: A Frequency Domain Approach, Second Edition also studies in detail the similarities and differences with the classical time domain approach. It high-lights many of the important steps in the identification process, points out the possible pitfalls to the reader, and illustrates the powerful tools that are available.
Unlike other books in this field, System Identification, Second Edition is ideal for practicing engineers, scientists, researchers, and both master's and PhD students in electrical, mechanical, civil, and chemical engineering. Click to read or download. This book presents a thorough description of methods to model linear dynamic timeinvariant systems by their transfer function. The relations between the transfer function and the physical parameters of the system are very dependent upon the specific problem.
Because transfer function models are generally valid, we have restricted the scope of the book to these alone, so as to develop and study general purpose identification techniques.
System identification : a frequency domain approach
This should not be unnecessarily restricting for readers who are more interested in the physical parameters of a system: the transfer function still contains all the information that is available in the measurements, and it can be considered to be an intermediate model between the measurements and the physical parameters. Also, the transfer function model is very suitable for those readers looking for a black box description of the input-output relations of a system. And, of course, the model is directly applicable to predict the output of the system. In this book, we use mainly frequency domain representations of the data.
In combination with periodic excitations, this opens many possibilities to identify continuous-time Laplace-domain or discrete-time z-domain models, if necessary extended with an arbitrary and unknown delay. Although we strongly advocate using periodic excitations, we also extend the methods and models to deal with arbitrary excitations. The "classical" timedomain identification methods that are specifically directed toward these signals are briefly covered and encapsulated in the identification framework that we offer to the reader. This book provides answers to questions at different levels, such as: What is identification and why do I need it?
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How to measure the frequency response function of a linear dynamic system? How to identify a dynamic system? All these are very basic questions, directly focused on the interests of the practitioner. 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? How to recognize, qualify, and quantify nonlinear distortions. How to select the excitation signals in order to get the best measurements.
ISBN 10: 0780360001
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. 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.
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.