Identification of Linear Systems

Identification of Linear Systems

In most cases the identification of linear systems is concerned with stochastic models where the inputs are observed without errors : the models use only one disturbing noise source , called process noise ( see Fig . 3 .

Author: J. Schoukens

Publisher: Elsevier

ISBN: 9780080912561

Category: Science

Page: 353

View: 958

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.
Categories: Science

Subspace Identification for Linear Systems

Subspace Identification for Linear Systems

input-output data Ulk, y'k Subspace Classical identification identification Reduced state • sequence High order model Least Model squares reduction Reduced model Reduced model Figure 1.6 System identification aims at constructing state ...

Author: Peter van Overschee

Publisher: Springer Science & Business Media

ISBN: 9781461304654

Category: Technology & Engineering

Page: 272

View: 809

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.
Categories: Technology & Engineering

Identification of Linear Systems by an Asymptotically Stable Observer

Identification of Linear Systems by an Asymptotically Stable Observer

The Markov parameters are the pulse response samples of a linear system . The fundamental idea in the developed identification procedure is to identify parameters of an observer rather than those of the actual system .

Author: Minh Q. Phan

Publisher:

ISBN: NASA:31769000445075

Category: Linear systems

Page: 69

View: 402

Categories: Linear systems

Identification of Linear Systems by an Asymptotically Stable Observer

Identification of Linear Systems by an Asymptotically Stable Observer

The Markov parameters are the pulse response samples of a linear system . The fundamental idea in the developed identification procedure is to identify parameters of an observer rather than those of the actual system .

Author:

Publisher:

ISBN: UIUC:30112106710343

Category: Linear systems

Page: 66

View: 410

Categories: Linear systems

System Identification SYSID 03

System Identification  SYSID  03

IDENTIFICATION OF LINEAR SYSTEMS WITH NONLINEAR DISTORTIONS J. Schoukens ( * ) , R. Pintelon ( * ) , T. Dobrowiecki ( ** ) , Y. Rolain ( * ) . ( * ) : Vrije Universiteit Brussel , dep . ELEC , Pleinlaan 2 , B1050 Brussels , Belgium ...

Author: Paul Van Den Hof

Publisher: Elsevier

ISBN: 0080437095

Category: Science

Page: 2088

View: 648

The scope of the symposium covers all major aspects of system identification, experimental modelling, signal processing and adaptive control, ranging from theoretical, methodological and scientific developments to a large variety of (engineering) application areas. It is the intention of the organizers to promote SYSID 2003 as a meeting place where scientists and engineers from several research communities can meet to discuss issues related to these areas. Relevant topics for the symposium program include: Identification of linear and multivariable systems, identification of nonlinear systems, including neural networks, identification of hybrid and distributed systems, Identification for control, experimental modelling in process control, vibration and modal analysis, model validation, monitoring and fault detection, signal processing and communication, parameter estimation and inverse modelling, statistical analysis and uncertainty bounding, adaptive control and data-based controller tuning, learning, data mining and Bayesian approaches, sequential Monte Carlo methods, including particle filtering, applications in process control systems, motion control systems, robotics, aerospace systems, bioengineering and medical systems, physical measurement systems, automotive systems, econometrics, transportation and communication systems *Provides the latest research on System Identification *Contains contributions written by experts in the field *Part of the IFAC Proceedings Series which provides a comprehensive overview of the major topics in control engineering.
Categories: Science

Robust Control of Linear Systems and Nonlinear Control

Robust Control of Linear Systems and Nonlinear Control

Identification of Linear Systems by Prony's Method G. Ammar D. Cheng C. Martin W. Dayawansa Abstract In this paper a variation of Prony's method is used in a problem of system identification . It is shown that methods developed for ...

Author: M. A. Kaashoek

Publisher: Springer Science & Business Media

ISBN: 0817634703

Category: Juvenile Nonfiction

Page: 655

View: 465

This volume is the second of the three volume publication containing the proceedings of the 1989 International Symposium on the Mathemat ical Theory of Networks and Systems (MTNS-89), which was held in Amsterdam, The Netherlands, June 19-23, 1989 The International Symposia MTNS focus attention on problems from system and control theory, circuit theory and signal processing, which, in general, require application of sophisticated mathematical tools, such as from function and operator theory, linear algebra and matrix theory, differential and algebraic geometry. The interaction between advanced mathematical methods and practical engineering problems of circuits, systems and control, which is typical for MTNS, turns out to be most effective and is, as these proceedings show, a continuing source of exciting advances. The second volume contains invited papers and a large selection of other symposium presentations in the vast area of robust and nonlinear control. Modern developments in robust control and H-infinity theory, for finite as well as for infinite dimensional systems, are presented. A large part of the volume is devoted to nonlinear control. Special atten tion is paid to problems in robotics. Also the general theory of nonlinear and infinite dimensional systems is discussed. A couple of papers deal with problems of stochastic control and filterina. vi Preface The titles of the two other volumes are: Realization and Modelling in System Theory (volume 1) and Signal Processing, Scattering and Operator Theory, and Numerical Methods (volume 3).
Categories: Juvenile Nonfiction

Errors in Variables Methods in System Identification

Errors in Variables Methods in System Identification

Automatica 35:91–100 Schoukens J, Vandersteen G, Pintelon R, Guillaume P (1999b) Frequency domain identification of linear systems using arbitrary excitations and a non-parametric noise model. IEEE Trans Autom Control 44(2):343–347 ...

Author: Torsten Söderström

Publisher: Springer

ISBN: 9783319750019

Category: Technology & Engineering

Page: 485

View: 211

This book presents an overview of the different errors-in-variables (EIV) methods that can be used for system identification. Readers will explore the properties of an EIV problem. Such problems play an important role when the purpose is the determination of the physical laws that describe the process, rather than the prediction or control of its future behaviour. EIV problems typically occur when the purpose of the modelling is to get physical insight into a process. Identifiability of the model parameters for EIV problems is a non-trivial issue, and sufficient conditions for identifiability are given. The author covers various modelling aspects which, taken together, can find a solution, including the characterization of noise properties, extension to multivariable systems, and continuous-time models. The book finds solutions that are constituted of methods that are compatible with a set of noisy data, which traditional approaches to solutions, such as (total) least squares, do not find. A number of identification methods for the EIV problem are presented. Each method is accompanied with a detailed analysis based on statistical theory, and the relationship between the different methods is explained. A multitude of methods are covered, including: instrumental variables methods; methods based on bias-compensation; covariance matching methods; and prediction error and maximum-likelihood methods. The book shows how many of the methods can be applied in either the time or the frequency domain and provides special methods adapted to the case of periodic excitation. It concludes with a chapter specifically devoted to practical aspects and user perspectives that will facilitate the transfer of the theoretical material to application in real systems. Errors-in-Variables Methods in System Identification gives readers the possibility of recovering true system dynamics from noisy measurements, while solving over-determined systems of equations, making it suitable for statisticians and mathematicians alike. The book also acts as a reference for researchers and computer engineers because of its detailed exploration of EIV problems.
Categories: Technology & Engineering

Advances in Neural Information Processing Systems 19

Advances in Neural Information Processing Systems 19

Right: Linear subspace identification method [5]. The broken lines represent the observations and the solid lines represent the simulated values. 6 Conclusion A new subspace method for learning nonlinear dynamical systems using ...

Author: Bernhard Schölkopf

Publisher: MIT Press

ISBN: 9780262195683

Category: Computers

Page: 1643

View: 639

The annual conference on NIPS is the flagship conference on neural computation. It draws top academic researchers from around the world & is considered to be a showcase conference for new developments in network algorithms & architectures. This volume contains all of the papers presented at NIPS 2006.
Categories: Computers