Heterogeneous Distributed Average Tracking

TL;DR

This paper develops two nonsmooth algorithms enabling heterogeneous agents with different dynamics to collaboratively track the average of time-varying references using only local information and interactions.

Contribution

It introduces novel distributed algorithms for heterogeneous agents, including a filtering approach to relax input assumptions and achieve average tracking.

Findings

Algorithms successfully track the average of reference inputs.

The second algorithm relaxes input restrictions via filtering.

Numerical examples validate the effectiveness of the proposed methods.

Abstract

This paper addresses distributed average tracking for a group of heterogeneous physical agents consisting of single-integrator, double-integrator and Euler-Lagrange dynamics. Here, the goal is that each agent uses local information and local interaction to calculate the average of individual time-varying reference inputs, one per agent. Two nonsmooth algorithms are proposed to achieve the distributed average tracking goal. In our first proposed algorithm, each agent tracks the average of the reference inputs, where each agent is required to have access to only its own position and the relative positions between itself and its neighbors. To relax the restrictive assumption on admissible reference inputs, we propose the second algorithm. A filter is introduced for each agent to generate an estimation of the average of the reference inputs. Then, each agent tracks its own generated signal to achieve the average tracking goal in a distributed manner. Finally, numerical example is included for illustration.