Multivariable System Identification For Process Control
eBook - ePub

Multivariable System Identification For Process Control

  1. 372 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Multivariable System Identification For Process Control

About this book

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. Matlabâ„¢ M-files, designed to help the reader to learn identification in a computing environment, are included.

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Yes, you can access Multivariable System Identification For Process Control by Y. Zhu in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Mechanical Engineering. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Inside Front Cover
  5. Copyright
  6. Dedication
  7. Foreword
  8. Preface
  9. Symbols and Abbreviations
  10. Chapter 1: Introduction
  11. Chapter 2: Models of Dynamic Processes and Signals
  12. Chapter 3: Identification Test Design and Data Pretreatment
  13. Chapter 4: Identification by the Least-Squares Method
  14. Chapter 5: Extensions of the Least-Squares Method
  15. Chapter 6: Asymptotic Method; SISO Case
  16. Chapter 7: Asymptotic Method; MIMO Case
  17. Chapter 8: Subspace Model Identification of MIMO Processes
  18. Chapter 9: Nonlinear Process Identification
  19. Chapter 10: Applications of Identification in Process Control
  20. Chapter 11: Model Based Fault Detection and Isolation
  21. Appendix A: Refresher on Matrix Theory
  22. Bibliography
  23. Index