Pattern analysis in networks of diffusively coupled Lur'e systems

Journal Article (2019)
Author(s)

Kirill Rogov (Eindhoven University of Technology)

A. Pogromsky (Eindhoven University of Technology)

E. Steur (TU Delft - Team Bart De Schutter)

W. Michiels (Katholieke Universiteit Leuven)

Henk Nijmeijer (Eindhoven University of Technology)

Research Group
Team Bart De Schutter
Copyright
© 2019 Kirill Rogov, Alexander Pogromsky, E. Steur, Wim Michiels, Henk Nijmeijer
DOI related publication
https://doi.org/10.1142/S0218127419502006
More Info
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Publication Year
2019
Language
English
Copyright
© 2019 Kirill Rogov, Alexander Pogromsky, E. Steur, Wim Michiels, Henk Nijmeijer
Research Group
Team Bart De Schutter
Issue number
14
Volume number
29
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Abstract

In this paper, a method for pattern analysis in networks of diffusively coupled nonlinear systems of Lur'e form is presented. We consider a class of nonlinear systems which are globally asymptotically stable in isolation. Interconnecting such systems into a network via diffusive coupling can result in persistent oscillatory behavior, which may lead to pattern formation in the coupled systems. Some of these patterns may coexist and can even all be locally stable, i.e. the network dynamics can be multistable. Multistability makes the application of common analysis methods, such as the direct Lyapunov method, highly involved. We develop a numerically efficient method in order to analyze the oscillatory behavior occurring in such networks. We focus on networks of Lur'e systems in which the oscillations appear via a Hopf bifurcation with the (diffusively) coupling strength as a bifurcation parameter and therefore display sinusoidal-like behavior in the neighborhood of the bifurcation point. Using the describing function method, we replace nonlinearities with their linear approximations. Then we analyze the system of linear equations by means of the multivariable harmonic balance method. We show that the multivariable harmonic balance method is able to accurately predict patterns that appear in such a network, even if multiple patterns coexist.

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