Applied System Identification
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Applied System Identification

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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.show more

Product details

  • Paperback | 400 pages
  • 178 x 230 x 24mm | 662.24g
  • Pearson Education (US)
  • Prentice Hall
  • Upper Saddle River, United States
  • English
  • Facsimile
  • Facsimile
  • 013079211X
  • 9780130792112
  • 1,654,588

Back cover copy

Effective system identification includes the underlying methodologies, computational procedures, and their implementation. To this end, this volume presents readers with the mathematical background required to participate in the growing field of system identification as applied to engineering systems. Author Jer-Nan Juang provides a common basis for understanding the techniques developed under various disciplines. In addition, he attempts to bring the discipline of system identification up to date. Specifically Applied System Identification: provides an overview of the disciplines of modal testing used in structural engineering and system identification; presents time- and frequency-domain models used in the disciplines of structures and controls; identifies basic concepts and properties of the frequency response function; features a unified mathematical framework based on the theory of system realization to correlate some of the existing time-domain methods commonly used in modal testing; introduces readers to a new way of interpreting the input/output relationship via an observer for identification of a system model and its corresponding observer to characterize system uncertainties; proposes a simple, yet effective way of curve-fitting the frequency response data and of constructing a system model via matrix-fraction description methods; considers the identification problem of a system operating in closed-loop with an existing feedback controller; develops a unified mathematical framework to derive recursive algorithms for the fast transversal filter and the least-squares lattice filter. Whether used as a textbook or as an addition to your personal reference library, Applied SystemIdentification offers an ideal opportunity to build a bridge between the disciplines of system identification as applied to controls and to modal testing.show more

About Jer-Nan Juang

Jer-Nan Juang (Ph.D., Virginia Polytechnic Institute and State University) is Principal Scientist in the Spacecraft Dynamics Branch, NASA Langley Research Center, and adjunct professor, George Washington University. He is the author of over 180 publications, including over 60 archival journal papers, and is the recipient of several NASA Outstanding Performance, Special Achievement, and Technical Excellence Awards. He currently coordinates joint technical activities with other Langley and NASA organizations, other Government agencies, and industry/universities on a national and international basis. He conducts personal analytical and experimental research in the fields of mechanical designs, controller designs and system identification for control of flexible structures including Robotics.show more

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.show more

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