Data-Driven Identification of Networks of Dynamic Systems

Inbunden, Engelska, 2022

1 616 kr

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2022-05-12
Mått:175 x 250 x 19 mm
Vikt:670 g
Format:Inbunden
Språk:Engelska
Antal sidor:286
Förlag:Cambridge University Press
ISBN:9781316515709

Utforska kategorier

Elektronik och kommunikationer inom Naturvetenskap och teknik

Mer om författaren

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 is a professor at Beijing Institute of Technology. Baptiste Sinquin is an algorithm engineer at SYSNAV.

Innehållsförteckning

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.