Heterogeneous distributed average tracking using nonsmooth algorithms

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.