Data-Driven Identification of Networks of Dynamic Systems

Michel Verhaegen; Chengpu Yu; Baptiste Sinquin

doi:10.1017/9781009026338

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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Contents

Frontmatter
pp i-iv
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Contents
pp v-viii
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Preface
pp ix-xi
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Vocabulary
pp xii-xiv
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Notation and Symbols
pp xv-xvi
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Acronyms
pp xvii-xviii
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1 - Introduction
pp 1-14
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Part I - Modelling Large-Scale Dynamic Networks
pp 15-16
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2 - Generic Modelling for MIMO LTI Systems
pp 17-23
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3 - Signal Flow Models of Dynamic Networks
pp 24-43
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4 - Models of Networks of Local LTI Systems
pp 44-66
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5 - Classification of Models of Networks of LTI Systems
pp 67-82
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Part II - The Identification Methods
pp 83-84
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6 - Identification of Signal Flow Dynamic Networks
pp 85-104
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7 - Subspace-Like Identification of Multi-Dimensional Systems
pp 105-128
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8 - Subspace Identification of Local Systems in an NDS
pp 129-188
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9 - Estimating Structured State-Space Models
pp 189-216
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Part III - Illustrating with an Application to Adaptive Optics
pp 217-218
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10 - Towards Control of Large-Scale Adaptive Optics Systems
pp 219-241
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11 - Conclusions
pp 242-244
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Appendices
pp 245-246
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A - Some Properties of the Kronecker Product
pp 247-249
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B - Matrices and Tensors
pp 250-256
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References
pp 257-265
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Index
pp 266-268
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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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