System Identification

Author: Lennart Ljung
Publisher: Pearson Education
ISBN: 9780132440530
Size: 11.89 MB
Format: PDF
View: 43

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.

Mastering System Identification In 100 Exercises

Author: Johan Schoukens
Publisher: John Wiley & Sons
ISBN: 9781118218501
Size: 13.95 MB
Format: PDF, ePub, Mobi
View: 38

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

Author: Rik Pintelon
Publisher: John Wiley & Sons
ISBN: 9780471660958
Size: 15.21 MB
Format: PDF, ePub
View: 36

Electrical Engineering System Identification A Frequency Domain Approach How does one model a linear dynamic system from noisy data? This book presents a general approach to this problem, with both practical examples and theoretical discussions that give the reader a sound understanding of the subject and of the pitfalls that might occur on the road from raw data to validated model. The emphasis is on robust methods that can be used with a minimum of user interaction. Readers in many fields of engineering will gain knowledge about: * Choice of experimental setup and experiment design * Automatic characterization of disturbing noise * Generation of a good plant model * Detection, qualification, and quantification of nonlinear distortions * Identification of continuous- and discrete-time models * Improved model validation tools and from the theoretical side about: * System identification * Interrelations between time- and frequency-domain approaches * Stochastic properties of the estimators * Stochastic analysis System Identification: A Frequency Domain Approach is written for practicing engineers and scientists who do not want to delve into mathematical details of proofs. Also, it is written for researchers who wish to learn more about the theoretical aspects of the proofs. Several of the introductory chapters are suitable for undergraduates. Each chapter begins with an abstract and ends with exercises, and examples are given throughout.

Applied System Identification

Author: Jer-Nan Juang
Publisher:
ISBN: 013079211X
Size: 13.59 MB
Format: PDF, Kindle
View: 61

Effective system identification includes the underlying methodologies, computational procedures, and their implementation. To this end, this volume presents readers with the mathematical background required to participate in the growing field of system identification as applied to engineering systems. Author Jer-Nan Juang provides a common basis for understanding the techniques developed under various disciplines. In addition, he attempts to bring the discipline of system identification up to date. Specifically Applied System Identification: provides an overview of the disciplines of modal testing used in structural engineering and system identification; presents time- and frequency-domain models used in the disciplines of structures and controls; identifies basic concepts and properties of the frequency response function; features a unified mathematical framework based on the theory of system realization to correlate some of the existing time-domain methods commonly used in modal testing; introduces readers to a new way of interpreting the input/output relationship via an observer for identification of a system model and its corresponding observer to characterize system uncertainties; proposes a simple, yet effective way of curve-fitting the frequency response data and of constructing a system model via matrix-fraction description methods; considers the identification problem of a system operating in closed-loop with an existing feedback controller; develops a unified mathematical framework to derive recursive algorithms for the fast transversal filter and the least-squares lattice filter. Whether used as a textbook or as an addition to your personal reference library, Applied System Identification offers an ideal opportunity to build a bridge between the disciplines of system identification as applied to controls and to modal testing.

Filtering And System Identification

Author: Michel Verhaegen
Publisher: Cambridge University Press
ISBN: 1139465023
Size: 17.28 MB
Format: PDF, ePub
View: 51

Filtering and system identification are powerful techniques for building models of complex systems. This 2007 book discusses the design of reliable numerical methods to retrieve missing information in models derived using these techniques. Emphasis is on the least squares approach as applied to the linear state-space model, and problems of increasing complexity are analyzed and solved within this framework, starting with the Kalman filter and concluding with the estimation of a full model, noise statistics and state estimator directly from the data. Key background topics, including linear matrix algebra and linear system theory, are covered, followed by different estimation and identification methods in the state-space model. With end-of-chapter exercises, MATLAB simulations and numerous illustrations, this book will appeal to graduate students and researchers in electrical, mechanical and aerospace engineering. It is also useful for practitioners. Additional resources for this title, including solutions for instructors, are available online at www.cambridge.org/9780521875127.

System Identification

Author: Karel J. Keesman
Publisher: Springer Science & Business Media
ISBN: 0857295225
Size: 12.58 MB
Format: PDF, Kindle
View: 41

System Identification shows the student reader how to approach the system identification problem in a systematic fashion. The process is divided into three basic steps: experimental design and data collection; model structure selection and parameter estimation; and model validation, each of which is the subject of one or more parts of the text. Following an introduction on system theory, particularly in relation to model representation and model properties, the book contains four parts covering: • data-based identification – non-parametric methods for use when prior system knowledge is very limited; • time-invariant identification for systems with constant parameters; • time-varying systems identification, primarily with recursive estimation techniques; and • model validation methods. A fifth part, composed of appendices, covers the various aspects of the underlying mathematics needed to begin using the text. The book uses essentially semi-physical or gray-box modeling methods although data-based, transfer-function system descriptions are also introduced. The approach is problem-based rather than rigorously mathematical. The use of finite input–output data is demonstrated for frequency- and time-domain identification in static, dynamic, linear, nonlinear, time-invariant and time-varying systems. Simple examples are used to show readers how to perform and emulate the identification steps involved in various control design methods with more complex illustrations derived from real physical, chemical and biological applications being used to demonstrate the practical applicability of the methods described. End-of-chapter exercises (for which a downloadable instructors’ Solutions Manual is available from fill in URL here) will both help students to assimilate what they have learned and make the book suitable for self-tuition by practitioners looking to brush up on modern techniques. Graduate and final-year undergraduate students will find this text to be a practical and realistic course in system identification that can be used for assessing the processes of a variety of engineering disciplines. System Identification will help academic instructors teaching control-related to give their students a good understanding of identification methods that can be used in the real world without the encumbrance of undue mathematical detail.