Distributed strategies for generating weight-balanced and doubly stochastic digraphs

TL;DR

This paper characterizes weight-balanced and doubly stochastic digraphs and introduces distributed algorithms to compute the necessary weights, facilitating their use in cooperative control applications.

Contribution

It provides a characterization of these digraph classes and develops distributed algorithms for weight computation, advancing control network design.

Findings

Characterization of weight-balanced and doubly stochastic digraphs

Distributed algorithms for weight computation

Application potential in cooperative control

Abstract

Weight-balanced and doubly stochastic digraphs are two classes of digraphs that play an essential role in a variety of cooperative control problems, including formation control, distributed averaging, and optimization. We refer to a digraph as doubly stochasticable (weight-balanceable) if it admits a doubly stochastic (weight-balanced) adjacency matrix. This paper studies the characterization of both classes of digraphs, and introduces distributed algorithms to compute the appropriate set of weights in each case.