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Data-Driven Identification of Networks of Dynamic Systems

Data-Driven Identification of Networks of Dynamic Systems

Data-Driven Identification of Networks of Dynamic Systems

Michel Verhaegen, Technische Universiteit Delft, The Netherlands
Chengpu Yu, Beijing Institute of Technology
Baptiste Sinquin, Sysnav
May 2022
Available
Hardback
9781316515709
NZD$229.95
inc GST
Hardback
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eBook

    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.

    • Provides a comprehensive overview of identifying network connected systems
    • Discusses key applications in large scale adaptive optics
    • Includes current open questions to prompt further research

    Product details

    May 2022
    Hardback
    9781316515709
    320 pages
    250 × 175 × 19 mm
    0.67kg
    Available

    Table of Contents

    • 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.
      Authors
    • Michel Verhaegen , Delft University of Technology

      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).

    • Chengpu Yu , Beijing Institute of Technology

      Chengpu Yu is a professor at Beijing Institute of Technology.

    • Baptiste Sinquin , Sysnav

      Baptiste Sinquin is an algorithm engineer at SYSNAV.