Decentralized Control of Multi-Agent Systems Under Acyclic Spatio-Temporal Task Dependencies

TL;DR

This paper presents a distributed control method for heterogeneous multi-agent systems that manages complex spatio-temporal tasks with acyclic dependencies, ensuring task fulfillment and communication among agents.

Contribution

A novel distributed sampled-data control approach for multi-agent systems with acyclic task dependencies, integrating task prioritization and communication maintenance.

Findings

Effective task prioritization in multi-agent control

Seamless communication among collaborating agents

Successful numerical simulation demonstration

Abstract

We introduce a novel distributed sampled-data control method tailored for heterogeneous multi-agent systems under a global spatio-temporal task with acyclic dependencies. Specifically, we consider the global task as a conjunction of independent and collaborative tasks, defined over the absolute and relative states of agent pairs. Task dependencies in this form are then represented by a task graph, which we assume to be acyclic. From the given task graph, we provide an algorithmic approach to define a distributed sampled-data controller prioritizing the fulfilment of collaborative tasks as the primary objective, while fulfilling independent tasks unless they conflict with collaborative ones. Moreover, communication maintenance among collaborating agents is seamlessly enforced within the proposed control framework. A numerical simulation is provided to showcase the potential of our control framework.