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 To order this title, and for more information, click here
By
Y. Zhu, Faculty EE, Eindhoven University of Technology, P O Box 513, 5600 MB Eindhoven, The Netherlands.
Description
Systems and control theory has experienced significant development in the past few decades. New techniques have emerged which hold enormous
potential for industrial applications, and which have therefore also attracted much interest from academic researchers. However, the
impact of these developments on the process industries has been limited.
The purpose of Multivariable System Identification
for Process Control is to bridge the gap between theory and application, and to provide industrial solutions, based on sound scientific
theory, to process identification problems. The book is organized in a reader-friendly way, starting with the simplest methods, and
then gradually introducing more complex techniques. Thus, the reader is offered clear physical insight without recourse to large amounts
of mathematics. Each method is covered in a single chapter or section, and experimental design is explained before any identification
algorithms are discussed. The many simulation examples and industrial case studies demonstrate the power and efficiency of process identification,
helping to make the theory more applicable. Matlab™ M-files, designed to help the reader to learn identification in
a computing environment, are included.
Audience
For academic and industrial engineers in all areas of systems and control engineering, especially process control.
Contents
Chapter headings.
Foreward. Preface. Symbols and Abbreviations. Introduction.
What is process identification? The hierarchy
of modern automation systems. Multivariable model-based process control. Outline of the book.
Models of Dynamic Processes and Signals.
SISO Continuous-time models. SISO Discrete-time models. MIMO models. Models of signals. Linear processes with disturbances. Nonlinear
models.
Identification Test Design and Data Pretreatment.
Controller configuration; selection of MV's, CV's and DV's. Preliminary
process tests. Test signals for final test, persistent excitation. Test for model identification, final test. Sampling frequency and
anti-aliasing filter. Pre-treatment of data. When is excitation allowed? Concluding remarks.
Identification by the Least Squares
Method.
The principle of least-squares. Estimating models of linear processess. Industrial case studies. Properties of the least-squares
estimator. Conclusions.
Extensions of the Least-Squares Method.
Modifying the frequency weighting by prefiltering. Output error
method. Instrumental Variable (IV) methods. Prediction error methods. User's choice in identification for control. More on order/structure
selection. Model validation. Identifying the glass tube drawing process. Recursive parameter estimation. Conclusions and discussion.
Asymptotic Method; SISO Case.
The Asymptotic theory. Optimal test signal spectrum for control. Parameter estimation and order
selection. Model validation using upper error bound. Simulation studies and conclusion.
Asymptotic Method; MIMO Case.
MIMO version
of the asymptotic theory. Asymptotic method. Identification of the glass tube drawing processes. Conclusions.
Subspace Model Identification
of MIMO Processes.
Introduction. Definition of the state space identification problem. Definition of the data equation. Analysis
of step response measurements. Subspace identification using generic input signals. Treatment of additive perturbations. A simulation
study. Summary of extensions.
Nonlinear Process Identification.
Identification of Hammerstein models. Identification of Wiener
models. Identification of NLN Hammerstein-Wiener model. Conclusions and recommendations.
Applications of Identification in Process
Control.
A project approach to advanced process control. Identification requirements for process control. PID autotuning using process
identification. Identification of I11-conditioned processes. Identification of a crude unit for MPC. Closed-loop identification of a
Deethanizer. Conclusions and perspectives.
Model Based Fault Detection and Isolation.
Introduction. Residuals for linear systems
with additive faults. Residuals for non additive faults in nonlinear systems. Residual evaluation. Industrial applications.
Refresher
on Matrix Theory.
Definitions and some basic properties of matrices. Eigenvalues and eigenvectors. The singluar value decomposition
and QR factorization. The Hankel matrix of a linear process.
Bibliography. Index.
Bibliographic & ordering Information
Hardbound, 372 pages, publication date: OCT-2001
ISBN-13: 978-0-08-043985-3
ISBN-10: 0-08-043985-3
Imprint: ELSEVIER
Price: Order form
GBP 105
USD 185
EUR 150
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Last update: 18 Jun 2008
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