A Consensus Control for a Multi-Agent System With Unknown Time-Varying Communication Delays

Janghoon Yang

doi:10.1109/access.2021.3070388

Janghoon Yang

Open Access

https://doi.org/10.1109/access.2021.3070388

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Journal: IEEE Access	Publication Date: Jan 1, 2021
Citations: 40	License type: CC BY 4.0

Affiliation: Seoul Media Institute of Technology

Abstract

A multi-agent system to have information flow among agents is bound to experience a communication delay over a directed graph. However, delay information is often unavailable and time varying. To deal with this problem, a consensus control for a second-order multi-agent system consisting of a leader and multiple followers is developed. Sliding mode control is exploited to deal with uncertainties incurred by unknown time varying communication delay and disturbance. The proposed sliding mode consensus control is shown to achieve the asymptotic bounded consensus and the finite time convergence of a sliding variable for any unknown time varying delay with the bounded first and the second derivative. The numerical simulations verify the properties of the proposed algorithm and its performance which is almost identical to one achieved by a consensus algorithm exploiting known delay information.

Highlights

With the widespread use of the internet of things, connected devices which are working at geographically different areas often work as a single complex system
The goal of this paper is to find a consensus control to achieve the asymptotic bounded consensus of the leader-follower multi-agent system (MAS) in the presence of disturbance and unknown time-varying delays
It is assumed that the consensus protocol starts at t = 1 to deal with delayed information

Summary

Introduction

With the widespread use of the internet of things, connected devices which are working at geographically different areas often work as a single complex system. The MAS may be controlled from a single controller with very powerful processing capability, which is called as a centralized control. This control strategy may have limited applicability due to the complexity which is proportional to the number of agents. On the other hand, decentralized control allows each agent to operate independently without sharing information, which may often incur the performance degradation compared to the centralized control. A distributed control which exploits information from the limited number of agents often provides the good tradeoff between the centralized control and the decentralized one [2]

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