Data-Driven Identification of Networks of Dynamic Systems

Michel Verhaegen; Chengpu Yu; Baptiste Sinquin

doi:10.1017/9781009026338

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Li, Shuang 2022. System Identification by Matching Pursuit and Orthogonal Matching Pursuit. p. 396.

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Fonken, Stefanie J.M. Ramaswamy, Karthik R. and Van Den Hof, Paul M.J. 2023. Local Identification in Dynamic Networks using a Multi-Step Least Squares Method. p. 431.

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Devisetty, Gowtham and Rani, D. Jenila 2024. Analysis and comparison for accurate prediction of innovative Bradycardia using linear discriminant analysis classifier and support vector machine. Vol. 3193, Issue. , p. 020101.

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Govindarajan, Nithin Chandrasekaran, Shivkumar and Dewilde, Patrick 2025. Tree Quasi-separable Matrices: A Simultaneous Generalization of Sequentially and Hierarchically Semiseparable Representations. SIAM Journal on Matrix Analysis and Applications, Vol. 46, Issue. 2, p. 1562.

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Michel Verhaegen, Technische Universiteit Delft, The Netherlands, Chengpu Yu, Beijing Institute of Technology, Baptiste Sinquin, Sysnav

Publisher:: Cambridge University Press
Online publication date:: April 2022
Print publication year:: 2022
Online ISBN:: 9781009026338
DOI:: https://doi.org/10.1017/9781009026338

Subjects:: Engineering, Electronic, Optoelectronic Devices, and Nanotechnology, Control Systems and Optimisation

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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.

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Data-Driven Identification of Networks of Dynamic Systems

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Contents

Frontmatter
pp i-iv

Contents
pp v-viii

Preface
pp ix-xi

Vocabulary
pp xii-xiv

Notation and Symbols
pp xv-xvi

Acronyms
pp xvii-xviii

1 - Introduction
pp 1-14

Part I - Modelling Large-Scale Dynamic Networks
pp 15-16

2 - Generic Modelling for MIMO LTI Systems
pp 17-23

3 - Signal Flow Models of Dynamic Networks
pp 24-43

4 - Models of Networks of Local LTI Systems
pp 44-66

5 - Classification of Models of Networks of LTI Systems
pp 67-82

Part II - The Identification Methods
pp 83-84

6 - Identification of Signal Flow Dynamic Networks
pp 85-104

7 - Subspace-Like Identification of Multi-Dimensional Systems
pp 105-128

8 - Subspace Identification of Local Systems in an NDS
pp 129-188

9 - Estimating Structured State-Space Models
pp 189-216

Part III - Illustrating with an Application to Adaptive Optics
pp 217-218

10 - Towards Control of Large-Scale Adaptive Optics Systems
pp 219-241

11 - Conclusions
pp 242-244

Appendices
pp 245-246

A - Some Properties of the Kronecker Product
pp 247-249

B - Matrices and Tensors
pp 250-256

References
pp 257-265

Index
pp 266-268

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Data-Driven Identification of Networks of Dynamic Systems

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