ORCID Identifier(s)

0000-0002-3240-6641

Graduation Semester and Year

2018

Language

English

Document Type

Thesis

Degree Name

Master of Science in Mechanical Engineering

Department

Mechanical and Aerospace Engineering

First Advisor

Frank Lewis

Second Advisor

Kamesh Subbarao

Abstract

With increased levels of autonomy in most of the engineering fields and booms in areas such as swarms, platoons and Internet of Things (IoT), communication and information flow has become a highly researched field. With advancements in autonomous vehicles (AVs) and drones in armed warfare, more and more focus is being laid on intercommunication between these vehicles and its surroundings as well as intra-communication among the fleets/swarms itself. It is easier to deal with individual agents whereas it is quite challenging to deal with multi-agent systems especially with highly dynamic agents. In this thesis, we propose a general protocol for dealing with such multi-agent systems and how to manage dynamic agents. The approach is preliminarily based on graph theory for distributed multi-agent consensus control and contagion spread from adversaries to the other agents is quarantined by methods of graph clustering. During the research, position consensus controller was experimentally verified and clustering methods were simulated on computer. A major focus of the research is on how to accommodate for parting of existing adversaries from the group and allow for the entry of new agents to the flock as and when required in time. This aspect of the research allows for mitigating risk factors associated with hacked agents and couple new agents (with similar motives to that of the flock) to the flock.

Keywords

Trust, Graph, Networks, Topology, Consensus, Controller, Distributed control, Connected vehicles, Autonomous, Clustering, Swarms, UAV, Confidence, Voting

Disciplines

Aerospace Engineering | Engineering | Mechanical Engineering

Comments

Degree granted by The University of Texas at Arlington

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