Identification Of Linear Systems
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Subspace Identification For Linear Systems
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Author : Peter van Overschee
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Subspace Identification For Linear Systems written by Peter van Overschee and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Technology & Engineering categories.
Subspace Identification for Linear Systems focuses on the theory, implementation and applications of subspace identification algorithms for linear time-invariant finite- dimensional dynamical systems. These algorithms allow for a fast, straightforward and accurate determination of linear multivariable models from measured input-output data. The theory of subspace identification algorithms is presented in detail. Several chapters are devoted to deterministic, stochastic and combined deterministic-stochastic subspace identification algorithms. For each case, the geometric properties are stated in a main 'subspace' Theorem. Relations to existing algorithms and literature are explored, as are the interconnections between different subspace algorithms. The subspace identification theory is linked to the theory of frequency weighted model reduction, which leads to new interpretations and insights. The implementation of subspace identification algorithms is discussed in terms of the robust and computationally efficient RQ and singular value decompositions, which are well-established algorithms from numerical linear algebra. The algorithms are implemented in combination with a whole set of classical identification algorithms, processing and validation tools in Xmath's ISID, a commercially available graphical user interface toolbox. The basic subspace algorithms in the book are also implemented in a set of Matlab files accompanying the book. An application of ISID to an industrial glass tube manufacturing process is presented in detail, illustrating the power and user-friendliness of the subspace identification algorithms and of their implementation in ISID. The identified model allows for an optimal control of the process, leading to a significant enhancement of the production quality. The applicability of subspace identification algorithms in industry is further illustrated with the application of the Matlab files to ten practical problems. Since all necessary data and Matlab files are included, the reader can easily step through these applications, and thus get more insight in the algorithms. Subspace Identification for Linear Systems is an important reference for all researchers in system theory, control theory, signal processing, automization, mechatronics, chemical, electrical, mechanical and aeronautical engineering.
Identification Of Linear Systems
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Author : J. Schoukens
language : en
Publisher: Elsevier
Release Date : 2014-06-28
Identification Of Linear Systems written by J. Schoukens and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-28 with Science categories.
This book concentrates on the problem of accurate modeling of linear systems. It presents a thorough description of a method of modeling a linear dynamic invariant system by its transfer function. The first two chapters provide a general introduction and review for those readers who are unfamiliar with identification theory so that they have a sufficient background knowledge for understanding the methods described later. The main body of the book looks at the basic method used by the authors to estimate the parameter of the transfer function, how it is possible to optimize the excitation signals. Further chapters extend the estimation method proposed. Applications are then discussed and the book concludes with practical guidelines which illustrate the method and offer some rules-of-thumb.
Identification Of Linear Systems By An Asymptotically Stable Observer
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Author :
language : en
Publisher:
Release Date : 1992
Identification Of Linear Systems By An Asymptotically Stable Observer written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Linear systems categories.
System Identification
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Author : Lennart Ljung
language : en
Publisher: Pearson Education
Release Date : 1998-12-29
System Identification written by Lennart Ljung and has been published by Pearson Education this book supported file pdf, txt, epub, kindle and other format this book has been release on 1998-12-29 with Technology & Engineering categories.
The field's leading text, now completely updated. Modeling dynamical systems — theory, methodology, and applications. Lennart Ljung's System Identification: Theory for the User is a complete, coherent description of the theory, methodology, and practice of System Identification. This completely revised Second Edition introduces subspace methods, methods that utilize frequency domain data, and general non-linear black box methods, including neural networks and neuro-fuzzy modeling. The book contains many new computer-based examples designed for Ljung's market-leading software, System Identification Toolbox for MATLAB. Ljung combines careful mathematics, a practical understanding of real-world applications, and extensive exercises. He introduces both black-box and tailor-made models of linear as well as non-linear systems, and he describes principles, properties, and algorithms for a variety of identification techniques: Nonparametric time-domain and frequency-domain methods. Parameter estimation methods in a general prediction error setting. Frequency domain data and frequency domain interpretations. Asymptotic analysis of parameter estimates. Linear regressions, iterative search methods, and other ways to compute estimates. Recursive (adaptive) estimation techniques. Ljung also presents detailed coverage of the key issues that can make or break system identification projects, such as defining objectives, designing experiments, controlling the bias distribution of transfer-function estimates, and carefully validating the resulting models. The first edition of System Identification has been the field's most widely cited reference for over a decade. This new edition will be the new text of choice for anyone concerned with system identification theory and practice.
Identification Of Linear Systems By An Asymptotically Stable Observer
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Author : National Aeronautics and Space Adm Nasa
language : en
Publisher:
Release Date : 2018-11-03
Identification Of Linear Systems By An Asymptotically Stable Observer written by National Aeronautics and Space Adm Nasa and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-03 with categories.
A formulation is presented for the identification of a linear multivariable system from single or multiple sets of input-output data. The system input-output relationship is expressed in terms of an observer, which is made asymptotically stable by an embedded eigenvalue assignment procedure. The prescribed eigenvalues for the observer may be real, complex, mixed real and complex, or zero. In this formulation, the Markov parameters of the observer are identified from input-output data. The Markov parameters of the actual system are then recovered from those of the observer and used to obtain a state space model of the system by standard realization techniques. The basic mathematical formulation is derived, and extensive numerical examples using simulated noise-free data are presented to illustrate the proposed method. Phan, Minh Q. and Horta, Lucas G. and Juang, Jer-Nan and Longman, Richard W. Langley Research Center...
Introduction To Mathematical Systems Theory
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Author : Christiaan Heij
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-12-18
Introduction To Mathematical Systems Theory written by Christiaan Heij and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-12-18 with Science categories.
This book provides an introduction to the theory of linear systems and control for students in business mathematics, econometrics, computer science, and engineering; the focus is on discrete time systems. The subjects treated are among the central topics of deterministic linear system theory: controllability, observability, realization theory, stability and stabilization by feedback, LQ-optimal control theory. Kalman filtering and LQC-control of stochastic systems are also discussed, as are modeling, time series analysis and model specification, along with model validation.
Identification Of Nonlinear Physiological Systems
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Author : David T. Westwick
language : en
Publisher: John Wiley & Sons
Release Date : 2003-08-28
Identification Of Nonlinear Physiological Systems written by David T. Westwick and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-08-28 with Technology & Engineering categories.
Significant advances have been made in the field since the previous classic texts were written. This text brings the available knowledge up to date. * Enables the reader to use a wide variety of nonlinear system identification techniques. * Offers a thorough treatment of the underlying theory. * Provides a MATLAB toolbox containing implementation of the latest identification methods together with an extensive set of problems using realistic data sets.
Modeling And Identification Of Linear Parameter Varying Systems
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Author : Roland Toth
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-06-13
Modeling And Identification Of Linear Parameter Varying Systems written by Roland Toth and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-06-13 with Technology & Engineering categories.
Through the past 20 years, the framework of Linear Parameter-Varying (LPV) systems has become a promising system theoretical approach to h- dle the controlof mildly nonlinear and especially position dependent systems which are common in mechatronic applications and in the process ind- try. The birth of this system class was initiated by the need of engineers to achieve better performance for nonlinear and time-varying dynamics, c- mon in many industrial applications, than what the classical framework of Linear Time-Invariant (LTI) control can provide. However, it was also a p- mary goal to preserve simplicity and “re-use” the powerful LTI results by extending them to the LPV case. The progress continued according to this philosophy and LPV control has become a well established ?eld with many promising applications. Unfortunately, modeling of LPV systems, especially based on measured data (which is called system identi?cation) has seen a limited development sincethebirthoftheframework. Currentlythisbottleneck oftheLPVfra- work is halting the transfer of the LPV theory into industrial use. Without good models that ful?ll the expectations of the users and without the und- standing how these models correspond to the dynamics of the application, it is di?cult to design high performance LPV control solutions. This book aims to bridge the gap between modeling and control by investigating the fundamental questions of LPV modeling and identi?cation. It explores the missing details of the LPV system theory that have hindered the formu- tion of a well established identi?cation framework.
Mastering System Identification In 100 Exercises
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Author : Johan Schoukens
language : en
Publisher: John Wiley & Sons
Release Date : 2012-04-02
Mastering System Identification In 100 Exercises written by Johan Schoukens and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-04-02 with Technology & Engineering categories.
This book enables readers to understand system identification and linear system modeling through 100 practical exercises without requiring complex theoretical knowledge. The contents encompass state-of-the-art system identification methods, with both time and frequency domain system identification methods covered, including the pros and cons of each. Each chapter features MATLAB exercises, discussions of the exercises, accompanying MATLAB downloads, and larger projects that serve as potential assignments in this learn-by-doing resource.
System Identification With Matlab Linear Models
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Author : Marvin L.
language : en
Publisher: Createspace Independent Publishing Platform
Release Date : 2016-10-23
System Identification With Matlab Linear Models written by Marvin L. and has been published by Createspace Independent Publishing Platform this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-23 with categories.
In System Identification Toolbox software, MATLAB represents linear systems as model objects. Model objects are specialized data containers that encapsulate model data and other attributes in a structured way. Model objects allow you to manipulate linear systems as single entities rather than keeping track of multiple data vectors, matrices, or cell arrays. Model objects can represent single-input, single-output (SISO) systems or multiple-input, multiple-output (MIMO) systems. You can represent both continuous- and discrete-time linear systems. The toolbox provides several linear and nonlinear black-box model structures, which have traditionally been useful for representing dynamic systems. This book develops the next tasks with linear models:* "Black-Box Modeling" * "Identifying Frequency-Response Models" * "Identifying Impulse-Response Models" * "Identifying Process Models" * "Identifying Input-Output Polynomial Models" * "Identifying State-Space Models" * "Identifying Transfer Function Models" * "Refining Linear Parametric Models"* "Refine ARMAX Model with Initial Parameter Guesses at Command Line"* "Refine Initial ARMAX Model at Command Line" * "Extracting Numerical Model Data" * "Transforming Between Discrete-Time and Continuous-Time Representations" * "Continuous-Discrete Conversion Methods" * "Effect of Input Intersample Behavior on Continuous-Time Models" * "Transforming Between Linear Model Representations" * "Subreferencing Models"* "Concatenating Models" * "Merging Models"* "Building and Estimating Process Models Using System Identification Toolbox* "Determining Model Order and Delay" 5* "Model Structure Selection: Determining Model Order and Input Delay" * "Frequency Domain Identification: Estimating Models Using Frequency Domain Data" * "Building Structured and User-Defined Models Using System Identification Toolbox"