
Data-Driven Identification of Networks of Dynamic Systems
Cambridge University Press
Published on 12. May 2022
Book
Hardback
286 pages
978-1-316-51570-9 (ISBN)
Description
This comprehensive text provides an excellent introduction to the state of the art in the identification of network-connected systems. It covers models and methods in detail, includes a case study showing how many of these methods are applied in adaptive optics and addresses open research questions. Specific models covered include generic modelling for MIMO LTI systems, signal flow models of dynamic networks and models of networks of local LTI systems. A variety of different identification methods are discussed, including identification of signal flow dynamics networks, subspace-like identification of multi-dimensional systems and subspace identification of local systems in an NDS. Researchers working in system identification and/or networked systems will appreciate the comprehensive overview provided, and the emphasis on algorithm design will interest those wishing to test the theory on real-life applications. This is the ideal text for researchers and graduate students interested in system identification for networked systems.
More details
Language
English
Place of publication
Cambridge
United Kingdom
Target group
College/higher education
Illustrations
Worked examples or Exercises
Dimensions
Height: 250 mm
Width: 175 mm
Thickness: 20 mm
Weight
677 gr
ISBN-13
978-1-316-51570-9 (9781316515709)
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Schweitzer Classification
Other editions
Additional editions

Michel Verhaegen | Chengpu Yu | Baptiste Sinquin
Data-Driven Identification of Networks of Dynamic Systems
E-Book
04/2022
Cambridge University Press
€136.99
Available for download
Persons
Michel Verhaegen is a professor at Delft University of Technology and a fellow of the International Federation of Automatic Control (IFAC). He co-authored Filtering and System Identification: A Least Squares Approach (Cambridge University Press, 2010).
Content
1. Introduction; Part I. Modelling Large-Scale Dynamic Networks: 2. Generic modelling for MIMO LTI systems; 3. Signal flow models of dynamic networks; 4. Models of networks of local LTI systems; 5. Classification of models of networks of LTI systems; Part II. The Identification Methods: 6. Identification of signal flow dynamic networks; 7. Subspace-like identification of multi-dimensional systems; 8. Subspace identification of local systems in an NDS; 9. Estimating structured state-space models; Part III. Illustrating with an Application to Adaptive Optics: 10. Towards control of large-scale adaptive optics systems; 11. Conclusions.