Phase and gain stability for adaptive dynamical networks

TL;DR

This paper develops a control-theoretic framework to analyze the stability of adaptive dynamical networks, providing local conditions for steady state stability applicable to various models including adaptive Kuramoto systems.

Contribution

It introduces a novel stability analysis method treating the network as a feedback system, enabling local stability conditions based on node and edge dynamics.

Findings

Recovered classic stability result for Kuramoto with inertia

Matched necessary and sufficient conditions for adaptive Kuramoto model

Applicable to a broad class of heterogeneous adaptive systems

Abstract

In adaptive dynamical networks, the dynamics of the nodes and the edges influence each other. We show that we can treat such systems as a closed feedback loop between edge and node dynamics. Using recent advances on the stability of feedback systems from control theory, we derive local, sufficient conditions for steady states of such systems to be linearly stable. These conditions are local in the sense that they are written entirely in terms of the (linearized) behavior of the edges and nodes. We apply these conditions to the Kuramoto model with inertia written in adaptive form, and the adaptive Kuramoto model. For the former we recover a classic result, for the latter we show that our sufficient conditions match necessary conditions where the latter are available, thus completely settling the question of linear stability in this setting. The method we introduce can be readily applied to a vast class of systems. It enables straightforward evaluation of stability in highly heterogeneous systems.