Consensus-based Communication-aware Formation
Control for a Mobile Multi-agent System
Sang Xing
Department of Mathematics
and Statistics
Portland State University
Portland, OR 97201
sxing@pdx.edu
Thomas Yang
Department of Electrical Engineering
and Computer Science
Embry-Riddle Aeronautical University
Daytona Beach, FL 32114
Tianyu.Yang@erau.edu
Houbing Song
Department of Electrical Engineering
and Computer Science
Embry-Riddle Aeronautical University
Daytona Beach, FL 32114
h.song@ieee.org
Abstract—Over the past few decades, unmanned aerial vehicle
(UAV) technology has played a significant role in military and
civilian applications. To meet the challenges of the future, in
addition to improving the functionality and utility of individual
aircraft, there is a need to consider how to develop more effective
UAV management and organizations. Consequently, among the
many developments in UAVs, formation control has become an
important concept in recent years. Formation control requires
multiple UAVs to adapt to the mission including generate a
formation, stay in formation, and change formation. In this paper,
we further constrain the formation controller model, not only
estimate the desired separation with acceptable accuracy but also
ensure a consensus among estimates. Thus, optimizes the overall
communication performance of a dynamical multi-agent system.
Index Terms—Unmanned Aerial Vehicles, Multi-agent
Systems, Communication-aware, Decentralized, Distributed,
Consensus-based,, Formation Control
I. INTRODUCTION
Due to the limited equipment can be carried by a single
UAV, it is necessary to send out multiple missions to com-
plete a more complex task. A swarm of UAVs operating in
formation can disperse equipment, split a complex task into
several simple tasks, and assign them to different UAVs in the
formation, which will perform the task more efficiently.
Multiple UAVs can carry different equipment and collab-
orate to accomplish tasks that cannot be accomplished by a
single UAV, such as high-precision positioning, multi-angle
imaging and theater communication relay. In classical forma-
tion control, agents typically perceive their absolute position
relative to the global coordinate system, and by actively control
agents’ absolute positions to achieve their desired formation
[1]. Communications between agents are usually assumed to
be ideal within a certain range of communication [2].
In this paper, we adopted ideas from [3], where author Li
constructs a communication-aware formation controller that
uses the communication channel quality, which is measured
locally by agents to guide agents into a desired formation.
Thus, it also optimizes the overall communication quality of
the formation system.
Inspired by [4], where the motion of each individual aircraft,
better known as agent, is imposed of kinematic unicycle
model with constraints on its airspeed and travel direction. We
further constrains the communication-aware formation control
to reach a consensus between any pair of connected agents.
The formation system is shown in Fig. 1. We first have an
interaction model at the communication layer, which takes the
agents’ position as input and outputs agents’ relative position
with respect to their neighboring agents. The coordinates of
this set of relative positions are then taken as the imput for
our control layer. A gradient-based controller is imposed at the
control layer, where the communication quality among agents
are reached at its maximum performance. We then have our
second controller, the consensus controller,that manages all
agents’ relative positions at the control layer, and outputs the
final control of agents with consensus of all agents traveling
in the desired direction.
The remainder of the paper are organized as follows. In
Section II, we describe the preparatory work in order to under-
stand the formation control process of a dynamical multi-agent
system. Section III presents the derivation of interaction model
at the communication layer. These two controllers construct a
final formation controller model, which is presented in Section
IV.Simulation results are delivered in Section V. Lastly, we
concludes the paper in Section VI.
Fig. 1. Schematic diagram of distance-based formation control
