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"The identification of network connected dynamic systems is currently a hot research topic within the community of systems and control. Other engineering areas, social sciences and system biology are putting a lot of effort in the study of network connected systems. Modeling such networks and the identification of these models from acquired measurements is crucial in the analysis or understanding of the dynamics. Based on these models, synthesis to modify the behavior of the network can also be performed. This book gives a unique overview of state of the art research in the field of identifying networks of linear dynamical systems. This overview combines many of the pioneering contributions from the authors with those of other researchers that play a crucial role in the development of this new field"--
List 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.
About the author
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.
