Identification Of Dynamic Systems

Author : Rolf Isermann,Marco Münchhof
Publisher : Springer Science & Business Media
Page : 705 pages
File Size : 47,9 Mb
Release : 2010-11-22
Category : Technology & Engineering
ISBN : 9783540788799

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Identification of Dynamic Systems by Rolf Isermann,Marco Münchhof Pdf

Precise dynamic models of processes are required for many applications, ranging from control engineering to the natural sciences and economics. Frequently, such precise models cannot be derived using theoretical considerations alone. Therefore, they must be determined experimentally. This book treats the determination of dynamic models based on measurements taken at the process, which is known as system identification or process identification. Both offline and online methods are presented, i.e. methods that post-process the measured data as well as methods that provide models during the measurement. The book is theory-oriented and application-oriented and most methods covered have been used successfully in practical applications for many different processes. Illustrative examples in this book with real measured data range from hydraulic and electric actuators up to combustion engines. Real experimental data is also provided on the Springer webpage, allowing readers to gather their first experience with the methods presented in this book. Among others, the book covers the following subjects: determination of the non-parametric frequency response, (fast) Fourier transform, correlation analysis, parameter estimation with a focus on the method of Least Squares and modifications, identification of time-variant processes, identification in closed-loop, identification of continuous time processes, and subspace methods. Some methods for nonlinear system identification are also considered, such as the Extended Kalman filter and neural networks. The different methods are compared by using a real three-mass oscillator process, a model of a drive train. For many identification methods, hints for the practical implementation and application are provided. The book is intended to meet the needs of students and practicing engineers working in research and development, design and manufacturing.

Author : P. P. J. van den Bosch,A. C. van der Klauw
Publisher : CRC Press
Page : 212 pages
File Size : 41,5 Mb
Release : 2020-12-17
Category : Mathematics
ISBN : 9780429605925

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Modeling, Identification and Simulation of Dynamical Systems by P. P. J. van den Bosch,A. C. van der Klauw Pdf

This book gives an in-depth introduction to the areas of modeling, identification, simulation, and optimization. These scientific topics play an increasingly dominant part in many engineering areas such as electrotechnology, mechanical engineering, aerospace, and physics. This book represents a unique and concise treatment of the mutual interactions among these topics. Techniques for solving general nonlinear optimization problems as they arise in identification and many synthesis and design methods are detailed. The main points in deriving mathematical models via prior knowledge concerning the physics describing a system are emphasized. Several chapters discuss the identification of black-box models. Simulation is introduced as a numerical tool for calculating time responses of almost any mathematical model. The last chapter covers optimization, a generally applicable tool for formulating and solving many engineering problems.

Author : Chengpu Yu,Lihua Xie,Michel Verhaegen,Jie Chen
Publisher : Springer Nature
Page : 273 pages
File Size : 42,7 Mb
Release : 2021-11-22
Category : Technology & Engineering
ISBN : 9789811675744

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Blind Identification of Structured Dynamic Systems by Chengpu Yu,Lihua Xie,Michel Verhaegen,Jie Chen Pdf

This book is intended for researchers active in the field of (blind) system identification and aims to provide new identification ideas/insights for dealing with challenging system identification problems. It presents a comprehensive overview of the state-of-the-art in the area, which would save a lot of time and avoid collecting the scattered information from research papers, reports and unpublished work. Besides, it is a self-contained book by including essential algebraic, system and optimization theories, which can help graduate students enter the amazing blind system identification world with less effort.

Author : Karel J. Keesman
Publisher : Springer Science & Business Media
Page : 323 pages
File Size : 41,6 Mb
Release : 2011-05-16
Category : Technology & Engineering
ISBN : 9780857295224

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System Identification by Karel J. Keesman Pdf

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.

Author : Lennart Ljung,Torkel Glad
Publisher : Unknown
Page : 402 pages
File Size : 50,7 Mb
Release : 2016
Category : Electronic
ISBN : 9144116888

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Modeling & Identification of Dynamic Systems by Lennart Ljung,Torkel Glad Pdf

Author : Juš Kocijan
Publisher : Springer
Page : 267 pages
File Size : 49,9 Mb
Release : 2015-11-21
Category : Technology & Engineering
ISBN : 9783319210216

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Modelling and Control of Dynamic Systems Using Gaussian Process Models by Juš Kocijan Pdf

This monograph opens up new horizons for engineers and researchers in academia and in industry dealing with or interested in new developments in the field of system identification and control. It emphasizes guidelines for working solutions and practical advice for their implementation rather than the theoretical background of Gaussian process (GP) models. The book demonstrates the potential of this recent development in probabilistic machine-learning methods and gives the reader an intuitive understanding of the topic. The current state of the art is treated along with possible future directions for research. Systems control design relies on mathematical models and these may be developed from measurement data. This process of system identification, when based on GP models, can play an integral part of control design in data-based control and its description as such is an essential aspect of the text. The background of GP regression is introduced first with system identification and incorporation of prior knowledge then leading into full-blown control. The book is illustrated by extensive use of examples, line drawings, and graphical presentation of computer-simulation results and plant measurements. The research results presented are applied in real-life case studies drawn from successful applications including: a gas–liquid separator control; urban-traffic signal modelling and reconstruction; and prediction of atmospheric ozone concentration. A MATLAB® toolbox, for identification and simulation of dynamic GP models is provided for download.

Author : Steven L. Brunton,J. Nathan Kutz
Publisher : Cambridge University Press
Page : 615 pages
File Size : 53,9 Mb
Release : 2022-05-05
Category : Computers
ISBN : 9781009098489

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Data-Driven Science and Engineering by Steven L. Brunton,J. Nathan Kutz Pdf

A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

Author : Silvio Simani,Cesare Fantuzzi,Ron J. Patton
Publisher : Springer Science & Business Media
Page : 294 pages
File Size : 49,5 Mb
Release : 2013-11-11
Category : Technology & Engineering
ISBN : 9781447138297

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Model-based Fault Diagnosis in Dynamic Systems Using Identification Techniques by Silvio Simani,Cesare Fantuzzi,Ron J. Patton Pdf

Safety in industrial process and production plants is a concern of rising importance but because the control devices which are now exploited to improve the performance of industrial processes include both sophisticated digital system design techniques and complex hardware, there is a higher probability of failure. Control systems must include automatic supervision of closed-loop operation to detect and isolate malfunctions quickly. A promising method for solving this problem is "analytical redundancy", in which residual signals are obtained and an accurate model of the system mimics real process behaviour. If a fault occurs, the residual signal is used to diagnose and isolate the malfunction. This book focuses on model identification oriented to the analytical approach of fault diagnosis and identification covering: choice of model structure; parameter identification; residual generation; and fault diagnosis and isolation. Sample case studies are used to demonstrate the application of these techniques.

Author : Oliver Nelles
Publisher : Springer Science & Business Media
Page : 786 pages
File Size : 52,8 Mb
Release : 2013-03-09
Category : Technology & Engineering
ISBN : 9783662043233

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Nonlinear System Identification by Oliver Nelles Pdf

Written from an engineering point of view, this book covers the most common and important approaches for the identification of nonlinear static and dynamic systems. The book also provides the reader with the necessary background on optimization techniques, making it fully self-contained. The new edition includes exercises.

Author : Goodwin
Publisher : Academic Press
Page : 303 pages
File Size : 53,5 Mb
Release : 1977-11-10
Category : Computers
ISBN : 9780080956459

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Dynamic System Identification: Experiment Design and Data Analysis by Goodwin Pdf

Dynamic System Identification: Experiment Design and Data Analysis

Author : Oliver Nelles
Publisher : Springer Nature
Page : 1235 pages
File Size : 40,9 Mb
Release : 2020-09-09
Category : Science
ISBN : 9783030474393

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Nonlinear System Identification by Oliver Nelles Pdf

This book provides engineers and scientists in academia and industry with a thorough understanding of the underlying principles of nonlinear system identification. It equips them to apply the models and methods discussed to real problems with confidence, while also making them aware of potential difficulties that may arise in practice. Moreover, the book is self-contained, requiring only a basic grasp of matrix algebra, signals and systems, and statistics. Accordingly, it can also serve as an introduction to linear system identification, and provides a practical overview of the major optimization methods used in engineering. The focus is on gaining an intuitive understanding of the subject and the practical application of the techniques discussed. The book is not written in a theorem/proof style; instead, the mathematics is kept to a minimum, and the ideas covered are illustrated with numerous figures, examples, and real-world applications. In the past, nonlinear system identification was a field characterized by a variety of ad-hoc approaches, each applicable only to a very limited class of systems. With the advent of neural networks, fuzzy models, Gaussian process models, and modern structure optimization techniques, a much broader class of systems can now be handled. Although one major aspect of nonlinear systems is that virtually every one is unique, tools have since been developed that allow each approach to be applied to a wide variety of systems.

Author : Yuri Tiumentsev,Mikhail Egorchev
Publisher : Academic Press
Page : 0 pages
File Size : 41,8 Mb
Release : 2019-05-17
Category : Science
ISBN : 0128152540

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Neural Network Modeling and Identification of Dynamical Systems by Yuri Tiumentsev,Mikhail Egorchev Pdf

Neural Network Modeling and Identification of Dynamical Systems presents a new approach on how to obtain the adaptive neural network models for complex systems that are typically found in real-world applications. The book introduces the theoretical knowledge available for the modeled system into the purely empirical black box model, thereby converting the model to the gray box category. This approach significantly reduces the dimension of the resulting model and the required size of the training set. This book offers solutions for identifying controlled dynamical systems, as well as identifying characteristics of such systems, in particular, the aerodynamic characteristics of aircraft.

Author : J.R. Raol,G. Girija,J. Singh
Publisher : IET
Page : 405 pages
File Size : 50,6 Mb
Release : 2004-08-13
Category : Mathematics
ISBN : 9780863413636

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Modelling and Parameter Estimation of Dynamic Systems by J.R. Raol,G. Girija,J. Singh Pdf

This book presents a detailed examination of the estimation techniques and modeling problems. The theory is furnished with several illustrations and computer programs to promote better understanding of system modeling and parameter estimation.

Author : Lennart Ljung,Torkel Glad
Publisher : Prentice Hall
Page : 0 pages
File Size : 47,5 Mb
Release : 1994
Category : Computer simulation
ISBN : 0135970970

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Modeling of Dynamic Systems by Lennart Ljung,Torkel Glad Pdf

Written by a recognized authority in the field of identification and control, this book draws together into a single volume the important aspects of system identification AND physical modelling. KEY TOPICS: Explores techniques used to construct mathematical models of systems based on knowledge from physics, chemistry, biology, etc. (e.g., techniques with so called bond-graphs, as well those which use computer algebra for the modeling work). Explains system identification techniques used to infer knowledge about the behavior of dynamic systems based on observations of the various input and output signals that are available for measurement. Shows how both types of techniques need to be applied in any given practical modeling situation. Considers applications, primarily simulation. MARKET: For practicing engineers who are faced with problems of modeling.

Author : Gianluigi Pillonetto,Tianshi Chen,Alessandro Chiuso,Giuseppe De Nicolao,Lennart Ljung
Publisher : Springer Nature
Page : 394 pages
File Size : 45,9 Mb
Release : 2022-05-13
Category : Computers
ISBN : 9783030958602

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Regularized System Identification by Gianluigi Pillonetto,Tianshi Chen,Alessandro Chiuso,Giuseppe De Nicolao,Lennart Ljung Pdf

This open access book provides a comprehensive treatment of recent developments in kernel-based identification that are of interest to anyone engaged in learning dynamic systems from data. The reader is led step by step into understanding of a novel paradigm that leverages the power of machine learning without losing sight of the system-theoretical principles of black-box identification. The authors’ reformulation of the identification problem in the light of regularization theory not only offers new insight on classical questions, but paves the way to new and powerful algorithms for a variety of linear and nonlinear problems. Regression methods such as regularization networks and support vector machines are the basis of techniques that extend the function-estimation problem to the estimation of dynamic models. Many examples, also from real-world applications, illustrate the comparative advantages of the new nonparametric approach with respect to classic parametric prediction error methods. The challenges it addresses lie at the intersection of several disciplines so Regularized System Identification will be of interest to a variety of researchers and practitioners in the areas of control systems, machine learning, statistics, and data science. This is an open access book.