A Study on Accelerating Average Consensus Algorithms Using Delayed Feedback

TL;DR

This paper proposes a method to accelerate Laplacian-based dynamic average consensus algorithms by optimally combining current and outdated feedback, leading to faster convergence without increasing control effort or steady-state error.

Contribution

It introduces a novel weighted feedback scheme using outdated information to enhance convergence rates in consensus algorithms, with analytical and numerical validation.

Findings

Identifies delay ranges that increase convergence rate

Shows outdated feedback can be used without increasing control effort

Determines optimal feedback weights for maximum acceleration

Abstract

In this paper, we study accelerating a Laplacian-based dynamic average consensus algorithm by splitting the conventional delay-free disagreement feedback into weighted summation of a current and an outdated term. We determine for what weighted sum there exists a range of time delay that results in the higher rate of convergence for the algorithm. For such weights, using the Lambert W function, we obtain the rate increasing range of the time delay, the maximum reachable rate and comment on the value of the corresponding maximizer delay. We also study the effect of use of outdated feedback on the control effort of the agents and show that only for some specific affine combination of the immediate and outdated feedback the control effort of the agents does not go beyond that of the delay-free algorithm. Additionally, we demonstrate that using outdated feedback does not increase the steady state tracking error of the average consensus algorithm. Lastly, we determine the optimum combination of the current and the outdated feedback weights to achieve the maximum increase in the rate of convergence without increasing the control effort of the agents. We demonstrate our results through a numerical example.