Multivariable System Identification For Process Control
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Table of contents
- Multivariable System Identification: for Porcess Controliii
- Copyright Pageiv
- Contentsxv
- Forewordvii
- Prefaceix
- Symbols and Abbreviationsxiii
- Chapter 1. Introduction1
- 1.1 What is Process Identification?1
- 1.2 The Hierarchy of Modern Automation Systems4
- 1.3 Multivariable Model-Based Process Control6
- 1.4 Outline of the Book13
- Chapter 2. Models of Dynamic Processes and Signals15
- 2.1 SISO Continuous-Time Models15
- 2.2 SISO Discrete-Time Models17
- 2.3 MIMO Models21
- 2.4 Models of Signals23
- 2.5 Linear Processes with Disturbances26
- 2.6 Nonlinear Models26
- Chapter 3. Identification Test Design and Data Pretreatment31
- 3.1 Controller Configuration; Selection of MV's, CV's and DV's32
- 3.2 Preliminary Process Tests37
- 3.3 Test Signals for Final Test, Persistent Excitation40
- 3.4 Test for Model Identification, Final Test53
- 3.5 Sampling Frequency and Anti-Aliasing Filter56
- 3.6 Pre-Treatment of Data59
- 3.7 When is Excitation Allowed? Concluding Remarks62
- Chapter 4. Identification by the Least-Squares Method65
- 4.1 The Principle of Least-Squares65
- 4.2 Estimating Models of Linear Processes67
- 4.3 Industrial Case Studies82
- 4.4 Properties of the Least-Squares Estimator88
- 4.5 Conclusions96
- Chapter 5. Extensions of the Least-Squares Method97
- 5.1 Modifying the Frequency Weighting by Prefiltering97
- 5.2 Output Error Method102
- 5.3 Instrumental Variable (IV) Methods110
- 5.4 Prediction Error Methods113
- 5.5 User's Choice in Identification for Control125
- 5.6 More on Order/Structure Selection135
- 5.7 Model Validation139
- 5.8 Identifying the Glass Tube Drawing Process142
- 5.9 Recursive Parameter Estimation151
- 5.10 Conclusions and Discussion157
- Chapter 6. Asymptotic Method; SISO Case161
- 6.1 The Asymptotic Theory162
- 6.2 Optimal Test Signal Spectrum for Control167
- 6.3 Parameter Estimation and Order Selection173
- 6.4 Model Validation using Upper Error Bound177
- 6.5 Simulation Studies and Conclusion178
- Chapter 7. Asymptotic Method; MIMO Case183
- 7.1 MIMO Version of the Asymptotic Theory183
- 7.2 Asymptotic Method186
- 7.3 Identification of the Glass Tube Drawing Processes191
- 7.4 Conclusions196
- Chapter 8. Subspace Model Identification of MIMO Processes199
- 8.1 Introduction199
- 8.2 Definition of the State Space Identification Problem200
- 8.3 Definition of the Data Equation201
- 8.4 Analysis of Step Response Measurements203
- 8.5 Subspace Identification using Generic Input Signals208
- 8.6 Treatment of Additive Perturbations211
- 8.7 A Simulation Study214
- 8.8 Summary of Extensions216
- Chapter 9. Nonlinear Process Identification217
- 9.1 Identification of Hammerstein Models218
- 9.2 Identification of Wiener Models231
- 9.3 Identification of NLN Hammerstein-Wiener Model241
- 9.4 Conclusions and Recommendations249
- Chapter 10. Applications of Identification in Process Control251
- 10.1 A Project Approach to Advanced Process Control251
- 10.2 Identification Requirements for Process Control256
- 10.3 PID Autotuning using Process Identification258
- 10.4 Identification of Ill-Conditioned Processes262
- 10.5 Identification of a Crude Unit for MPC277
- 10.6 Closed-Loop Identification of a Deethanizer284
- 10.7 Conclusions and Perspectives290
- Chapter 11. Model Based Fault Detection and Isolation293
- 11.1 Introduction293
- 11.2 Residuals for Linear Systems with Additive Faults295
- 11.3 Residuals for Non Additive Faults in Nonlinear Systems304
- 11.4 Residual Evaluation309
- 11.5 Industrial Applications317
- Appendix A. Refresher on Matrix Theory329
- A.1 Definitions and Some Basic Properties of Matrices329
- A.2 Eigenvalues and Eigenvectors331
- A.3 The Singular Value Decomposition and QR Factorization331
- A.4 The Hankel Matrix of a Linear Process333
- Bibliography335
- Index345
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.
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.