From Static to Dynamic Couplings in Consensus and Synchronization among Identical and Non-Identical Systems

Peter Wieland

ISBN 978-3-8325-2638-2
174 Seiten, Erscheinungsjahr: 2010
Preis: 35.50 €
From Static to Dynamic Couplings in Consensus and Synchronization among Identical and Non-Identical Systems
In a systems theoretic context, the terms 'consensus' and 'synchronization' both describe the property that all individual systems in a group behave asymptotically identical, i.e., output or state trajectories asymptotically converge to a common trajectory. The objective of the present thesis is an improved understanding of some of the diverse coupling mechanisms leading to consensus and synchronization.

A starting point is the observation that classical consensus and synchronization results commonly deal with two distinct facets of the problem: Consensus has regularly a strong focus on the interconnections and related constraints while synchronization typically addresses questions about complex individual dynamical systems. Very few results exist that address both facets simultaneously. A thorough analysis of static couplings in consensus algorithms provides explanations for this observation by unveiling limitations inherent to this type of couplings.

Novel dynamic coupling mechanisms are proposed to overcome these limitations. These methods essentially rely on an internal model principle for consensus and synchronization derived in the thesis. This principle provides necessary conditions for consensus and synchronization in groups of non-identical systems, and it establishes a link to the output regulation problem. The fresh point of view revealed by this link eventually leads to a new hierarchical mechanism for consensus and synchronization among complex non-identical systems with weak assumptions on the interconnections. Applications include synchronization of linear systems and phase synchronization of nonlinear oscillators.

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  • Synchronization
  • Consensus
  • Internal Model Principle
  • Diffusive Couplings
  • Communication Graphs


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