Tutorial on dynamic average consensus: the problem, its applications, and the algorithms

TL;DR

This tutorial provides a comprehensive overview of dynamic average consensus algorithms, explaining their problem formulation, applications, and key design considerations for distributed multi-agent systems.

Contribution

It offers an accessible introduction to the main ideas, performance trade-offs, and convergence analysis of dynamic average consensus algorithms.

Findings

Summarizes key algorithms and their properties.

Highlights trade-offs between convergence speed and accuracy.

Provides insights into design requirements for guaranteed performance.

Abstract

This paper considers the problem of dynamic average consensus algorithm design for a group of communicating agents. This problem consists of designing a distributed algorithm that enables a group of agents with communication and computation capabilities to use local interactions to track the average of locally time-varying reference signals at each agent. The objective of this article is to provide an overview of the dynamic average consensus problem that serves as a comprehensive introduction to the problem definition, its applications, and the distributed methods available to solve them. Our primary intention, rather than providing a full account of all the available literature, is to introduce the reader, in a tutorial fashion, to the main ideas behind dynamic average consensus algorithms, the performance trade-offs considered in their design, and the requirements needed for their analysis and convergence guarantees.