design and implementation of modern control algorithms for unmanned aerial vehicles

232 Pages·2014·5.82 MB·English

DESIGN AND IMPLEMENTATION OF
MODERN CONTROL ALGORITHMS FOR
UNMANNED AERIAL VEHICLES
by
A T H
HMED AIMOUR AFEZ
A thesis submitted to the
Graduate Program in Electrical & Computer Engineering
in conformity with the requirements for
the degree of Doctor of Philosophy
Queen’s University
Kingston, Ontario, Canada
December 2014
Copyright (cid:13)c AHMED TAIMOUR HAFEZ, 2014
Abstract
Recently, Unmanned Aerial Vehicles (UAVs) have attracted a great deal of attention
in academic, civilian and military communities as prospective solutions to a wide
variety of applications. The use of cooperative UAVs has received growing interest in
the last decade and this provides an opportunity for new operational paradigms.
As applications of UAVs continue to grow in complexity, the trend of using mul-
tiple cooperative UAVs to perform these applications rises in order to increase the
overall effectiveness and robustness.
There is a need for generating suitable control techniques that allow for the real-
time implementation of control algorithms for different missions and tactics executed
byagroupofcooperativeUAVs. Inthisthesis,weinvestigatepossiblecontrolpatterns
and associated algorithms for controlling a group of autonomous UAVs in real-time
to perform various tactics.
This research proposes new control approaches to solve the dynamic encirclement,
tactic switching and formation problems for a group of cooperative UAVs in simu-
lation and real-time. Firstly, a combination of Feedback Linearization (FL) and
decentralized Linear Model Predictive Control (LMPC) is used to solve the dynamic
encirclement problem. Secondly, a combination of decentralized LMPC and fuzzy
i
logic control is used to solve the problem of tactic switching for a group of coop-
erative UAVs. Finally, a decentralized Learning Based Model Predictive Control
(LBMPC) is used to solve the problem of formation for a group of cooperative UAVs
in simulation.
We show through simulations and validate through experiments that the proposed
control policies succeed to control a group of cooperative UAVs to achieve the desired
requirements and control objectives for different tactics. These proposed control poli-
cies provide reliable and effective control techniques for multiple cooperative UAV
systems.
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Acknowledgments
First and foremost, I thank ALLAH (God), the creator, for his mercy, help and guid-
ance.
I was so fortunate to be supervised by both Dr. Shahram Yousefi and Dr. Sid-
ney Givigi. Their knowledge and guidance in research, and exceptional personalities
have been an extraordinary source of support throughout my research. There are no
words that express my sincere gratitude for their outstanding support.
Also, I would like to thank Professor Alain Beaulieu for his helpful suggestions during
the different stages of this research. Special thanks to my colleagues in my research
group, Autonomous Robotics Research (ARRG), for their support, collaboration,
friendship, fruitful discussions, and insightful feedback: Mohamed Iskandrani, David
Pike, James Jessup, David Hung, Riley Magee and Peter Jardine. It was pleasure to
work with them in this wonderful working environment.
I would like to thank my wife, Amira, and my son, Eyad, for their patience and
their support, and for the sacrifices they had to make. Their encouragement and
support are key ingredients for any achievements I have ever made. I owe my wife
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a sincere gratitude for her support and encouragement. She was the true sailer who
drove the family’s ship with perseverance and patience over the too many hardships
we have faced throughout my quest of knowledge. Her tolerance and understanding
of my changing mood are testaments of her unyielding devotion and love. I am also
in debt to my kid Eyad for his unwavering love and paramount support. I could never
have dreamed of a better family.
Also, I wish to express my love and gratitude to my beloved parents; although you
are thousands of miles away, you were always there whenever I needed you. I am
thankful to them for their inspiration and encouragement, and for teaching me to be
a lifelong student and seeker of knowledge.
Finally, I would like to thank my country, Egypt, for funding and supporting my
research, especially the Egyptian Armed Forces, my sponsor, for the unwavering sup-
port. Also, I would like to thank my Supervisor Dr. Sidney Givigi for the additional
funding from his Autonomous Robotics Research funding.
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Dedication
v
Co-Authorship
• Chapter 4
The study was initiated by Dr. S.N. Givigi. Mr. A.J. Marasco solved the
dynamic problem for a team of cooperative UAVs using nonlinear model pre-
dictive control in simulation and introduced a theoretical proof for the stability
of the controller. However, this proof neglected final terminal constraints and,
therefore, would not hold in the general case. The author worked in improv-
ing the proof of stability by adding the final terminal constraint and made the
necessary changes such that the proof holds. The author performed system
identification to acquire a linear model for the UAV, and derived the control
formulation based on linear model predictive control combined with feedback
linearization for the dynamic encirclement problem. Furthermore, The author
has validated the formulation in simulations and real time experiments. Mr.
Mohamed Iskandarani has helped the author in the experimental setup and
with the real time on-board implementation in the Qball-X4 quadrotors. The
authorwrotethefirstdraftofthemanuscriptsundertheclosesupervisionofDr.
S.N. Givigi and Dr. S. Yousefi. Chapter 4 has been accepted for publication
in IEEE Transactions on Control Systems Technology. Some of the prelimi-
nary results from Chapter 4 were published in the Proceedings of the American
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Control Conference (ACC), 2014.
• Chapter 5
The study was initiated by Dr. S.N. Givigi. The author performed all the
simulations and laboratory experiments for solving the tactic switching problem
for a group of cooperative UAVs using linear model predictive control. Mr.
Mohamed Iskandarani has helped the author in the experimental setup. The
authorwrotethefirstdraftofthemanuscriptsundertheclosesupervisionofDr.
S.N. Givgi and Dr. S. Yousefi. Chapter 5 has been submitted for publication
in IEEE Systems Journal. Some of the preliminary results from Chapter 5
were published in the Proceedings of The World Congress of the International
Federation of Automatic Control (IFAC), 2014.
• Chapter 6
The study was initiated by Dr. S.N. Givigi. The author performed all the sim-
ulations solving the formation problem for a group of cooperative UAVs using
learning based model predictive control and introduced a theoretical proof for
thestabilityofthecontroller. Theauthorwrotethefirstdraftofthemanuscripts
under the close supervision of Dr. S.N. Givgi and Dr. S. Yousefi. Chapter 6 has
been submitted for publication in IEEE Transactions on Aerospace and Elec-
tronic Systems. Some of the preliminary results from Chapter 6 were submitted
to the IEEE Robotics and Automation (ICRA) conference, 2015.
REFERENCES
[1] A.T.Hafez, A. J. Marasco, S. N. Givigi, A. Beaulieu, and C. A. Rabbath, “En-
circlement of multiple targets using model predictive control,” in American Control
vii
Conference (ACC), 2013. IEEE, 2013, pp. 3147-3152.
[2] M. Iskandarani, A.T.Hafez, S. Givigi, A. Beaulieu, and C. A. Rabbath, “Using
multiple quadrotor aircraft and linear model predictive control for the encirclement
of a target,” in IEEE International Systems Conference (SysCon) , IEEE, 2013, pp.
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[3] A.T.Hafez, M. Iskandarani, S. Givigi, S. Yousefi, C. A. Rabbath, and A.
Beaulieu, “Using linear model predictive control via feedback linearization for dy-
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viii
Rabbath, Solving Multi-UAV Dynamic Encirclement via Model Predictive Control ,”
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to IEEE International Systems Conference (IEEE SysCon), 2015.
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mation via DLBMPC,” submitted to IEEE Robotics and Automation (ICRA), 2015.
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UsingLearningBasedModelPredictiveControl,”inIEEE Transactions on Aerospace
and Electronic Systems.
[14] A.T.Hafez, S. Givigi, and S. Yousefi, “Formation Reconfiguration of Coop-
erative UAVs via Learning Based Model Predictive Control in an Obstacle-Loaded
Environment,” submitted to European Control Conference (ECC 2015).
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