Data-Driven Identification of Networks of Dynamic Systems

A comprehensive introduction to identifying network-connected systems, covering models and methods, and applications in adaptive optics.

Michel Verhaegen (Author), Chengpu Yu (Author), Baptiste Sinquin (Author)

9781316515709, Cambridge University Press

Hardback, published 12 May 2022

320 pages
25 x 17.5 x 1.9 cm, 0.67 kg

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.

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.

Subject Areas: Automatic control engineering [TJFM]