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Applied System Identification

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Applied System Identification


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  • most methods presented in this book are based on a popular MATLAB program called SOCIT.
  • builds a bridge between the disciplines of system identification as applied to controls and to modal testing — combines the solid theoretical and methodological foundations from the control field with the extensive experimental knowledge from the modal testing field.
  • uses a “result-example” style — using physical and intuitive arguments wherever possible.
  • presents real systems, such as the Hubble Spacecraft Telescope and an active aircraft wing, to illustrate some of the system identification techniques used in practice.


  • Copyright 1994
  • Dimensions: 7" x 9-1/4"
  • Pages: 416
  • Edition: 1st
  • Book
  • ISBN-10: 0-13-079211-X
  • ISBN-13: 978-0-13-079211-2

System identification is the process of developing or improving a mathematical representation of a physical system using experimental data. Over the past decade, several system identification techniques have been developed within different disciplines. This text/reference brings together the significant advances over the past decade into a single unified source — with common mathematical notation that will enable readers from a variety of engineering areas — e.g., aerospace, electrical, civil, and mechanical engineering —to apply system identification to engineering systems.
Focuses on the three types of identification in engineering structures — modal parameter identification; structural-model parameter identification; and control-model identification. MARKET: For researchers and engineers, students, and teachers in vibrations, controls and system identification.

Sample Content

Table of Contents

1. Introduction.

2. Time-Domain Models.

3. Frequency-Domain Models.

4. Frequency Response Functions.

5. System Realization.

6. Observer Identification.

7. Frequency Domain System ID.

8. Observer/Controller ID.

9. Recursive Techniques.

Appendix A: Fundamental Matrix Algebra.

Appendix B: Random Variables and Kalman Filter.

Appendix C: Data Acquisition.


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