Conference proceedings

ICASE - 2015
Fourth International Conference on
Aerospace Science & Engineering
September 2-4, 2015
Institute of Space Technology
Islamabad Pakistan
Fourth International Conference on Aerospace Science & Engineering (ICASE 2015) Proceedings

Conference Proceedings
Editors
Dr. Najam Abbas Naqvi
Mr. Raza Butt
ISBN 978-1-4673-9123-8
Printed in Pakistan
2016

i
ICASE ORGANIZING COMMITTEE
Engr. Imran Rahman (Chairman)
Dr. Najam Abbas Naqvi (Secretary)
Mr. Zia Sarwar (Treasurer)
Dr. Zafar Mohammad Khan
Dr. Muddassar Farooq
Dr. Badar Munir Ghauri
Dr. Qamar-ul-Islam
Dr. Abid Ali Khan
Dr. Ibrahim Qazi
Dr. Farrukh Chishtie
Dr. Asif Israr
Dr. Salman Ahmed
Dr. Mirza Muhammad Naseer
Engr. Ishaat Saboor
Engr. Khurram Humaiyun
Mr. Muhammad Hafeez
INTERNATIONAL SCIENTIFIC COMMITTEE
Dr. Leonardo Reynari , Italy
Dr Dongkai Yang, China
Dr. DDGL Dahanayaka , Sri Lanka
Dr. Ali Imran , United Kingdom
Dr. Muhammad Yusof Ismail , Malaysia
Dr. Rakhshan Roohi , Australia
Dr. Fawad Inam , United Kingdom
Dr. Tahir I. Khan , Canada
Dr. Iftikhar Ahmad , Saudi Arabia
Dr. Aquib Moin, South Africa
NATIONAL SCIENTIFIC COMMITTEE
Dr. Badar Munir Ghauri
Dr. Muddassar Farooq
Dr. Qamar-ul-Islam
Dr. Abid Ali Khan
Dr. Asif Israr
Dr. Ibrahim Qazi
Dr. Farrukh Chishtie
Dr. Syed Wilayat Hussain
Dr. Ahtezaz Qamar
Dr. Muhammad Zubair Khan
Dr. Umer Iqbal Bhatti
Dr. Aamir Habib
Dr. Khurram Khurshid
Dr. Moazam Maqsood
Dr. Fazeel Mehmood Khan
Dr. Waqas Qazi
Dr. Rizwan Mughal
Dr. Arjumand Zaidi
Dr. Abdul Haseeb

ii
EDITORIAL COMMITTEE
Mr. Raza Butt
Mr. Waqas Ramzan
ICASE SECRETARIAT
Dr. Najam Abbas (Secretary ICASE 2015)
Mr. Zeeshan Fareed (Marketing and Publicity)
Mr. Raza Butt (Media and Public Relations)
Mr. Waqas Jilani Joiya (Logistics and Operations)
Mr. Muhammad Adeel (Graphic Designing)
Mr. Waqas Ramzan (Data Management)

iii
SPONSORS
Higher Education Commission (HEC)
National ICT R&D Fund
COMSTECH
Pakistan Atomic Energy Commission (PAEC)
Kahuta Research Laboratory (KRL)
Pakistan Science Foundation (PSF)
Vital Group

iv
PREFACE
Institute of Space Technology (IST), Islamabad, Pakistan organized the “Fourth International
Conference on Aerospace Science & Engineering” (ICASE) from September 2-4, 2015. This
conference is a regular biennial event to provide an international forum in which researchers,
engineers, professional and students from all over the world get a chance to interact and discuss
the latest themes and trends related with aerospace science and engineering. It provides a
platform to share experiences, foster collaborations across industry and academia, and to
evaluate emerging aerospace technologies and developments across the globe.
The success of the first three conferences in 2009, 2011 and 2013 has earned ICASE a high
standing in the domains of high performance aerospace materials, space communication
techniques, control and guidance systems, design and construction of space systems and
structures. These conferences provided an ideal opportunity for exchange of information
amongst scientists, engineers and researchers from all across the globe.
ICASE 2015 featured a diverse blend of thematic areas including Aerospace and Avionics,
Satellite Design Development and Security, Mechanical Engineering for Aerospace
Applications, Aerospace Materials Design and Engineering, Satellite Communication and Image
Processing, Global Navigation Satellite Systems, Remote Sensing & Geographic Information
Science, Astronomy and Astrophysics, Information Technology and Cyber Security, Space
Technology Awareness and Society.
A total of 110 papers were presented in the conference while 30 poster presentations were held.
There were 20 technical sessions during the conference covering the different themes and track
related with aerospace science and engineering. In addition to that, there were 15 panel
discussions, tutorials and workshops sessions in connection with conference themes. A galaxy of
30 national and International invited speakers shared their research accomplishments with the
academicians, researchers and students from all over Pakistan.
The representatives from industry and elite Research and Development organizations also
exhibited their industrial and technical paraphernalia during the conference. Extensive
deliberations and collaborations were the other significant focuses of ICASE 2015. Key
representations included National ICT R&D Fund, AIDL and the National Space Agency of
Pakistan, SUPARCO. Main sponsors of ICASE 2015 included Higher Education Commission
(HEC), PAEC, KRL, COMSTECH, Pakistan Science Foundation, National ICT R&D Fund and
the VITAL group.
Prodigious efforts were put in to publish this ICASE 2015 proceedings book. Organizing
committee, reviewers, chairs, co-chairs, data processors, proofreaders and the designers
contributed their ration remarkably in pooling the valuable research findings in a single
document. I am grateful to ICASE 2015 team for their extended efforts in making this
conference a great success. My special thanks to our sponsors for their generous financial
support in driving the research zeal amongst the researchers, scientists and the engineers’
community.
Secretary
ICASE 2015

v
CONTENTS
1 Junaid Anwar A Comparison Study of Advanced state Observers for Quad rotor UAV
with Sliding Mode Control
1
2 Bushra Aijaz Fuzzy Temperature Controller for Induction Heating 9
3 Qazi Ejaz ur Rehman Stability and Control Solution of Quad-Copters 13
4 Amna Butt Detection of Fire Hotspots dealt by Emergency First Responders in
Rawalpindi using GIS application
20
5 Zeeshan Khan Prospects of Airborne Wind Energy Systems in Pakistan 25
6 Muhammad Amin Integrated use of Potential Rainwater Harvesting Site for Agriculture
Using Geo-Spatial Approach
31
7 Muhammad Usman Saleem Urban change detection of Lahore (Pakistan) using the Thematic
Mapper Images of Landsat since 1992-2010
38
8 Asad Abbas A Review of Fundamentals of Hyperspectral Imaging and its
Applications
44
9 Khazar Hayat A numerical study on the impact resistance of braided composites 50
10 Waheed Gul Improving physical and mechanical properties of medium density fiber
board (MDF)
57
11 Abdur Rehman Finite Element Analysis of Tool Wear in Ultrasonically Assisted
Turning
64
12 Rabia Zafar Metamaterials in Aerospace Industry: Recent Advances and Prospects 69
13 Engr Numan Khan Finite Element Simulation of Composite Body Armor 73
14 Atiq ur rehman Incorporate GNSS with Android & Improve the Search and Rescue
15 Malik Abid Hussain Geostatistical Analysis on Seismic Data over North-Western Regions of
Pakistan, Afghanistan and Eastern Regions of Iran & Tajikistan
16 Rabia Tabassum GIS for estimating Optimized Water Demand Using Sustainable Water
Resource ManagmentFor Planned City
17 Ferheen Ayaz Optimized Threshold Calculation based on Received Signal
Characteristics for Blanking Non-linearity at OFDM Receivers
18 Farzan Javed Sheikh A Review on Mobile Wireless Communication Networks (0G to
upcoming generations)
19 Muhammad Altaf Khan Intelligent Detection of Distributed Flooding Attack in Wireless Mesh
Network
21 Ferheen Ayaz Introducing space plant biology to students through hands-on activities
using clinostat
22 Faizan Muhammad The Political and Economic Feasibility of Current Space Resource
Management Policies
23 M Sohail Shahid Feasibility Study to Install Fire Fighting Equipment on a Cargo
Helicopter
24 M. Saad Sohail Design and Optimization of S-band Wilkinson Power Divider for
Transceiver Applications
25 Mateen Tariq Optimize Manufacturing of unidirectional carbon prepregs for space
Applications
26 Taimoor Zahid Electrical Power Conditing Unit Design for Space Qualified C Band
Receiver Geo Satellite Applications
27 Najam ud Din Ahmad DSP based Electro Hydraulic actuator control with irreteraceable
feedback error.
79
operation.
84
98
109
114
120
20 Aamir Nawaz Touch panel Based Restaurent Automation Using Zigbee Technology 126
131
136
147
154
157
160
165
Concept
28 Syed Jahanzeb Hussain Pirzada Design for Test Approach using FPGA for BPSK Modem 171
29 Taimoor Zahid Design of a Fuzzy Logic Water Level Controller 175
30 Gohar Ali The use of Nuclear reactor in Space Applications:Propulsion and Power 181

vi
31 Muhammad Aamir Impact of Thermal Aging on Microstructure and Mechanical Properties
of high Sn Content, Sn-Pb Solders
32 Madni Shifa Ullah Khan Effect of aryl diazonium salt functionlization on the electrical properties
of MWCNTs and MWCNTs/CF reinforced polymer composite
33 Sania Nazir Design of C band Slotted Waveguide Array antenna with high
Impedance Bandwidth and improved Reflection Coefficient
34 Muhammad Nauman Hussain Risk Areas Mapping and Identification of Hotspots on the Road-
Network of Lahore
36 Ali Jan Hassan Assessment of Urban Growth of Karachi: From A Tiny Town to A Meta
City of the World
38 Shakeel Ahmad Waqas Identification of Post Disaster Scenario Using Double Threshold Energy
Detection
39 M Ameer Umar Malik Design and Analysis of Magnetic MEMS Accelerometer for Inertial
Navigation
47 Syed Wasif Ali Shah Design and Development of Low Cost Motor Drive for Hub Wheel
based Electric Vehicles.
48 Shahid Karim Preparation, Structure and Dielectric/Piezoelectric Properties of BiScO3-
PbTiO3-Pb(Mn1/3Nb2/3)O3 high temperature piezoelectric ceramics
50 Muhammad Shoaib Comparison of Maximum Likelihood Classification Before and After
Applying Weierstrass Transform
51 Hira Fatima Spatio -Temporal Analysis of Shoreline Changes along Makran Coast
Using Remote Sensing and Geographical Information System
52 Sadaf Javed Influence Analysis of Minerals on Drinking Water Quality Around
River Jhelum
55 Zehra Ali Optimized Threshold Calculation based on Received Signal
Characteristics for Blanking Non-linearity at OFDM Receivers
56 Naveed Riaz Measurement & Testing Techniques of Performance Parameters for
Electric Servo Actuators
187
192
200
203
35 Muhammad Arslan Analysis of Recent Drought Based on NDVI and Meteorological Data 208
211
37 Abdur Raqeeb Gaziani JUPITER, The Gas GIANT 218
219
222
Fuzzy Logic
40 M Shahan Qamar Feasibility Assessment of Running JP-8 Fuel in Diesel Engine 229
41 Muhammad Usman Saleem Artificial intelligence reboot 236
42 Izhar An Intelligent Approach for Edge Detection in Noisy Images Using 243
Mode Neural Network augmentation.
43 Sundus Najib A Survey of Active ITU-R P-Series Propagation Models 249
44 Anwar Ul Haque An Experimental Study To Evaluate the Effect of Strut and Fairing 255
45 M Tanveer Iqbal Fairing Separation Dynamic Analysis Using Analytical Approach 261
46 Saqib Alam Launch Vehicle Control based on Dynamic Inversion with Sliding 265
271
275
49 Abdur Rasheed A Comprehensive Study On QoS For Mobile Ad Hoc Network 282
289
296
316
53 Iqra Basit Selection of the optimal interpolation method for groundwater quality 325
54 M Tasawer Hussain Thermal Design and Analysis of PNSS-1 Satellite 334
340
345
57 IEEE Publications LIST of 27 IEEE submitted Papers 348

Nonlinear Observers for Closed loop Sliding Mode
Control of Quadrotor UAV
Junaid Anwar, Fahad Mumtaz Malik, Muhammad Bilal Khan
College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Pakistan
Abstract—This paper deals with the performance comparison
of Sliding mode observer with super-twisting algorithm (STSMO)
and High gain Observer (HGO) for a remotely controlled quad
rotor UAV. Under the restriction that inertial co-ordinates and
attitude angles are available for measurement while angular
and linear velocities are estimated. This paper is solved in
two steps for each observer. First the observer (HGO and
STSMO) is designed and then in second stage a second order
(2-SMC) technique is being applied on the basis of estimated
states to design controller(for which systems is portioned into
fully and under-actuated sub-systems).Simulations results shows
the performance comparison of both observers under the same
control scheme.
I. INTRODUCTION
More recently, a growing interest in the UAV has been
shown by industry and academia [1]–[7].The vital and poten-
tial use of flying robots for civil as well as military applications
are attracting the industries and the academia community. The
feature of flying in narrow space and vertical takeoff and land-
ing (VTOL) made quadrotor unique relative to other mobile
robots and conventional aircrafts. The quadrotor is an under
actuated system with six outputs and four inputs, they are
owed to carry out the tasks ranging from surveillance to rescue
mission but the challenges behind the control of quadrotor
aerial vehicle like un-stability and highly nonlinear behavior
are the major source of attraction and many control approaches
to deal with quadrotor dynamics have been presented so far
[8]–[14][14-20].
This paper deals with the development of 2-sliding mode
control scheme that can cater for the model uncertainties,
external disturbances and the chattering phenomenon. Non-
availability of states is a major constraint towards accomplish-
ment of any control scheme, using sensor for each state is
also not feasible due to space limitations and high cost of the
sensors. Even with the availability of all states system model
generally shows parametric mismatch with respect to the
real time environment. These model imperfections, un-certain
initialization and sensor errors also degrades the performance
of the controller. The solution for that is to use state observers,
to estimate the states in real time, Luenberger [15] proves to be
good in the state estimation but these model based observers
fail when the system parameters keep on changing with the
time. Least square and recursive least square (RLS) are also
not able to work on highly nonlinear system such as quadrotor.
A high gain observer was first introduced by Khalil and
Esfandiaro [16] for the design of output feedback controllers
and asymptotic convergence. Researchers have contributed in
the development of their idea towards high gain observer [17]–
[20].High gain observers performance degrades in the presence
of external noise and is shown in simulation section of this
paper, due to which we have to look for an observer which is
robust to sensor noise.
The sliding mode observers are widely used because of their
prominent features like finite time convergence, robustness to
sensor noise and un-certain estimation [21], [22].Asymptotic
second order sliding mode observers were also developed but
they require proof of separation principle.High accuracy and
reduction in chattering are the main features of second order
sliding mode compared to the classical first order motion.
Recently a class of second order sliding mode observer is
introduced so called super-twisting observer [23] for second
order mechanical system which include quadrotor too. Super-
twisting observers can reconstruct the states if the perturbation
is of relative degree two, or reconstructs the perturbation itself,
when it is of relative degree one in finite time.
Aim of this paper is to compare the performance of both ob-
servers namely high-gain observer and super-twisting sliding
mode observer under same set of perturbations, uncertainties
and noise, so that each observer can exhibit its characteristics
for the quadrotor system. Real time estimation always require
knowledge of the pros and cons of the observer relative to the
particular system, so that best observer can be deployed for
real time estimation of states. So there is a need to explore
the type of observers which are application specific.
In the following section model of the quadrotor is developed
and presented. In section-III controller design is presented. In
section-IV observers are designed for the quadrotor model. In
section-V numerical simulation is given and finally in section-
V1 the conclusions are given.
II. DYNAMIC MODELING
A quad rotor UAV is a highly nonlinear dynamical system
and its modeling it is not an easy task due to under actua-
tion. It consists of two pairs of rotors which are moving in
opposite directions to provide the collective thrust as shown
in following fig 1. There are four inputs to this system.
The input U1 is the sum of thrusts provided by individual
rotors. The pitch movement is obtained by changing speeds
of rotors 2 and 4. Similarly the roll movement is achieved by
varying speeds of rotor 1 and 3. These two former operations
should be performed while keeping the total thrust constant
1

otherwise system may lose altitude and crash. The roll and
pitch movements are controlled by using inputs U2 and U3
respectively. The yaw movement occurs due to difference
between torques of the two pairs of rotors. This movement
is stabilized by using input U4.
Fig. 1. Quad Rotor UAV Free Body Diagram
Let us consider an inertial frame of reference E” and a body
fixed frame B” as shown in above figure. The transformation
between E and B is provided by a matrix R which is given by
the following equation.
xn
yn
zn
=
cos θ cos ψ − cos φ sin ψ + sin φ sin θ cos ψ sin φ sin ψ + cos φ cos ψ sin θ
cos θ sin ψ cos φ cos ψ + sin φ sin θ sin ψ sin θ sin ψ cos φ − cos ψ sin φ
sin θ cosθ sin φ cosθ cos φ
xb
yb
zb
xn
yn
zn
= R
xb
yb
zb
The Newton-Euler formalism is used to present the dynam-
ics of quad rotor UAV. The Newtons laws of motion when
applied to a rigid body in the presence of external forces and
torques are given by following set of equations
msI3∗3 O
O I
˙V
˙w
+
w ∗ msV
w ∗ Iw
=
F
τ
where ms is the mass of the quadrotor, V vector of xyz,
w be the vector of φ, θ and ψ,I represents a inertia vector
Ix Iy Iz
T
across x, y and z respectively.τ is torque
vector include roll torque,pitch torque and yaw torque and
F = 0 0 U1
T
.To convert the above equations in inertial
frame we use transformation matrix to get the following
equations
˙ξ = v
˙v = R
F
ms
˙R = R ˆw
J ˙w = −w ∗ Jw + τ
The crude and approximate model of quad rotor UAV from
above set of equations can be written as follows
˙ξ = v
˙v = ge3 + Re3
b
ms
Ω2
i
˙R = R ˆw
J ˙w = −w ∗ Iw − Jr w ∗ e3 Ωi + τ
where d is some modeling co-efficient, e3 is a vector
0 0 1
T
,r is the rotor co-efficient, ξ is the position vector,
R is the transformation matrix, ˆw is the skew-symmetric
matrix, Ω is the rotor speed, I is the inertial tensor matrix,
Jr is the rotor inertia, while Jp and Jm are the propeller and
motor inertia respectively and b is the thrust co-efficient.
The torques applied to the quad rotor’s axis is the difference
between the torques provided by the rotors on the other axis.
τ =
lb Ω2
4 − Ω2
2
lb Ω2
3 − Ω2
1
d Ω2
2 + Ω2
4 − Ω2
3 − Ω2
1
The rotor inertia consists of motor inertia, propeller inertia
and negligible reversing gearbox inertia and is given by the
following equation
Jr = Jp − Jmr
Now the complete six degrees of freedom model is given
by the following system of equations:-
¨x = cos φ sin θ cos ψ + sin φ sin ψ
U1
m
ÿ = cos φ sin θ sin ψ − sin φ cos ψ
U1
m
¨z = −g +
cos φ cos θ
ms
U1
¨φ = ˙θ ˙ψ
Iy − Iz
Ix
−
Jr
Ix
Ωr
˙θ +
l
Ix
U2
¨θ = ˙φ ˙ψ
Iz − Ix
Iy
−
Jr
Iy
Ωr
˙φ +
l
Iy
U3
¨ψ = ˙θ ˙φ
Ix − Iy
Iz
+
C
Iz
U4
(1)
where C is the proportional constant.The first term on right
hand side of first dynamical equation is the gyroscopic effect
caused by the rotation of the rigid body and the second term
is due to the propulsion effect. The system inputs are U1, U2,
U3 and U4.
The inputs are given by the following equations
U1 = b Ω2
2 + Ω2
4 − Ω2
3 − Ω2
1
U2 = b Ω2
4 − Ω2
2
U3 = b Ω2
3 − Ω2
1
U4 = d Ω2
2 + Ω2
4 − Ω2
3 − Ω2
1
Ω = d Ω4 + Ω2 − Ω3 − Ω1
2

To make the model more realistic especially in forward
flight we include the hub forces, rolling moments and variable
aerodynamics coefficients [24].
The hub force is the resultant of horizontal forces acting on
all blade elements.
H = CHρA ΩRrad
2
where CH is the hub coefficient, ρ is the air density and A is
the propeller disk area and Rrad is the propeller radius and ρ
is the speed of the respective propeller.Additionally the drag
moment Q is the moment about the rotor shaft caused by the
aerodynamic forces acting on the blades. In fact drag moments
determine the power required to spin the rotors. It is given by
the following equation
Q = CQρA ΩRrad
2
Rrad
where CQ is the drag coefficient.The rolling moment of
a propeller exists in forward flight when advancing blade
is producing more lift than the retreating blade. It is the
integration over the entire rotor of the lift of each section
acting at a given radius and is given by following equation.
Rm = CRm
ρA ΩRrad
2
Rrad
Where CRm is the rolling moment coefficient.Furthermore the
UAVs operating near the ground (approximately at half rotor
diameter) experience thrust augmentation due to better rotor
efficiency. This is related to a reduction of induced airflow
velocity. This is called Ground Effect. The following equation
represents the ground effect near the surface. It is assumed
that ground effect acts on the UAV when the UAV is below a
certain altitude,zo.
Fgr z =
A
z + zcg)2
−
A
zo + zcg)2
0 < z ≤ zo
After incorporating the above effects and the friction terms,
we obtain a more realistic model of the quad rotor UAV which
is given by the following set of equations:-
¨x = cos φ sin θ cos ψ + sin φ sin ψ
U1
m
−
1
m
4
i=1
Hxi − K1
˙x
ms
ÿ = cos φ sin θ sin ψ − sin φ sin ψ
U1
m
−
1
m
4
i=1
Hyi − K2
˙y
ms
¨z = −g +
cos φ cos θ
ms
U1 + Fgr z − K3
z
ms
¨φ = ˙θ ˙ψ
Iy − Iz
Ix
−
Jr
Ix
Ωr
˙θ +
l
Ix
U2 −
h
Ix
4
i=1
Hyi+
(−1)i+1
Ix
4
i=1
Rms
xi − lK4
˙φ
Ix
¨θ = ˙φ ˙ψ
Iz − Ix
Iy
−
Jr
Iy
Ωr
˙φ +
l
Iy
U3 −
h
Iy
4
i=1
Hyi+
(−1)i+1
Iy
4
i=1
Rms
yi − lK5
˙θ
Iy
¨ψ = ˙θ ˙φ
Ix − Iy
Iz
+
C
Iz
U4 +
h
Iz
4
i=1
Hyi+
l
Iz
Hx2 − Hx4 + Hy3 − Hy1
4
i=1
Qiyi−
lK6
˙ψ
Iz
(2)
III. CONTROLLER DESIGN
Let X = x, ˙x, y, ˙y, z, ˙z, φ, ˙φ, θ, ˙θ, ψ, ˙ψ,
T
and U =
U1, U2, U3, U4
T
be the state and control input vectors re-
spectively. The equation set (1) with the addition of friction
term and ground effect term for altitude can be written in state
space representation such as:
˙x1 = x2
˙x2 = cos x7 sin x9 cos x11 + sin x7 sin x11
U1
m
− K1
x2
ms
(3)
˙x3 = x4
˙x4 = cos x7 sin x9 sin x11 − sin x7 sin x11
U1
m
− K2
x4
ms
(4)
˙x5 = x6
˙x6 = −g +
cos x7 cos x9
ms
U1 + Fgr z − K3
x6
ms
(5)
3

˙x7 = x8
˙x8 = x10x12
Iy − Iz
Ix
−
Jr
Ix
Ωrx10 +
l
Ix
U2 − lK4
x8
Ix
(6)
˙x9 = x10
˙x10 = x10x12
Iz − Ix
Iy
−
Jr
Iy
Ωrx8 +
l
Iy
U3 − lK5
x10
Iy
(7)
˙x11 = x12
˙x12 = x10x8
Ix − Iy
Iz
+
C
Iz
U4 + lK6
x12
Iz
(8)
The term represents the ground effect near the surface. It is
assumed that the ground effect acts on the UAV when the UAV
is below a certain altitude. The goal is to design a second
order sliding mode control which is done in two steps. 1)
Choice of sliding surface w.r.t tracking error e. 2) Design
of Lyapunov function that guarantees negative definiteness
so that asymptotic convergence is achieved. Closed loop
control system dynamics become insensitive to modeling error,
perturbation signals and parameter variation as a by-product of
sliding mode control (SMC). Control efforts are calculated by
the help of Lyapunov analysis and hence guarantee asymptotic
convergence. As quadcopter has 4 inputs while number of
variables to be controlled are more than four hence overall it
is an under actuated system but we can portioned that system
into two parts namely fully actuated part and under-actuated
part and then designing the control for each part of the system
independently. Therefore control is also portioned into two
sub-system.
A. Control for fully actuated subsystem
Fully-actuated subsystem composed up of ¨z and ¨φ subsys-
tems (5) and (6) respectively. Choice of sliding surface for the
subsystem (6) comes out from the Lyapunov analysis as:
V =
e2
φ
2
where eφ = φd − φ
˙V = eφ ˙eφ
˙V = eφ
˙φd − ˙φ
So to make ˙V negative definite
˙φ = ˙φd + α1 φd − φ
Hence Surface S1 will be
S1 = ˙φd − ˙φ + α1 φd − φ
Let the Lyapunov function be
V =
S2
1
2
˙V = S1
˙S1
= S1
¨φd − ¨φ − α1
˙φd − ˙φ where α1 > 0
= S1
¨φd − ˙θ ˙ψ
Iy − Iz
Ix
−
Jr
Ix
˙θΩr
−
l
Ix
U2 + K4l
˙φ
Ix
+ α1
˙φd − ˙φ
To make it negative definite choice of input is as:
U2 =
Ix
l
¨φd − ˙θ ˙ψ
Iy − Iz
Ix
−
Jr
Ix
˙θΩr
+ K4l
˙φ
Ix
+ α1
˙φd − ˙φ + k1sat S1 + k2S1
where k1, k2 > 0
Similarly in the same way surface for subsystem (5) comes out
to be the linear combination of position and velocity tracking
errors of z state.
S2 = ˙zd − ˙z + α2 zd − z where α2 > 0
And in the same way by the Lyapunov analysis of surface the
control comes out to be
U1 =
ms
cos φ cos θ
¨zd + g + K3
˙z
ms
+ α2 ˙zd − ˙z
+ k2sat S2 + k4S2 + Fgr
where k3, k4 > 0
Where
B. Control for Under-actuated subsystem
Under-actuated subsystem composed up of ¨x, ÿ, ¨θ and ¨ψ
subsystems. The Choice of sliding surface for the subsys-
tem (3) and (7) comes out from the Lyapunov analysis as:
V =
e2
x
2
+
e2
θ
2
where ex = xd − x and eθ = θd − θ
˙V = ex ˙ex + eθ ˙eθ
˙V = ex ˙xd − ˙x + eθ
˙θd − ˙θ
So to make ˙V negative definite
˙x = ˙xd + α3 xd − x and ˙θ = ˙θd + α4 θd − θ
˙V < −α3 xd − x
2
− α4 θd − θ
2
where α3, α4 > 0
Hence Surface S3 will be
S3 = ˙xd − ˙x + xd − x + ˙θd − ˙θ + θd − θ
Let the Lyapunov function be
V =
S2
3
2
˙V = S3
˙S3
= S3 ¨xd − ¨x + α3 ˙xd − ˙x + ¨θd −
Iz − Ix
Iy
˙φ ˙ψ +
Jr
Iy
˙φΩr
−
l
Iy
U3 + K5
l ˙θ
Iy
+ α4
˙
θd − ˙θ
4

To make it negative definite choice of input is as:
U3 =
Iy
l
¨θd − ˙φ ˙ψ
Iz − Ix
Iy
−
Jr
Iy
Ωr
˙φ − lK5
˙θ
Iy
+ α3 ˙xd − x
+ α4
˙θd − ˙θ + ¨xd − ¨x + k5sat(S3) + k6S3
where k5, k6 > 0
Similarly in the same way surface for subsystem (4)
and eqref3f comes out to be the linear combination of position
and velocity tracking errors of two states i.e. y and ψ.
S4 = ˙yd − ˙y + α5 yd − y + ˙ψd − ˙ψ + α6 ψd − ψ
where α5, α6 > 0
and in the same way by the Lyapunov analysis of surface S4
the control U4 comes out to be
U4 =
Iz
c
¨ψd − ˙φ ˙θ
Ix − Iy
Iz
− lK6
˙ψ
Iz
+ α5 ˙yd − ˙y
− α6
˙ψd − ˙ψ + ÿd − ÿ + k7sat(S4) + k8S4
where k7, k8 > 0
U1 is the control input of z, U2 is the control input of roll,U3
is the control input of pitch and U4 is the control input
of yaw while motion in x and y direction is produced by
help of control inputs of roll, pitch and z. By the help of
U1, U2, U3 and U4 the desired trajectories are achieved and
tracking errors are reduced to zero asymptotically, by virtue
of Sliding mode controller.by keeping the roll and pitch angles
to zero controller robustly stabilize the UAV and move it to the
desired position with a desired yaw angle. The control scheme
is developed and implemented independent of observer and is
shown in the block diagram The controller is designed by
keeping in view the mathematical model of the quad rotor as
given in Section-II without any effects except friction and the
ground effect but that U1, U2, U3 and U4 are capable enough to
tackle not only Rolling Moments, Drag moments, Gyroscopic
effects, Hub forces but also retain its performance which is
being shown in the simulations section in this paper.
Fig. 2. Controller designed independent of the observer
IV. OBSERVER DESIGN
Two types of nonlinear observers are implemented for the
quad rotor system with the same control scheme i.e. 2-Sliding
mode Control. Observability is ensured by [25] for each block
of equation from (3)–(8) separately.
A. High Gain Observer
HGO is basically an approximate differentiator. This ob-
server works well for a wide class of nonlinear systems and
leads to recovery of the performance achieved under state
feedback. Implementation of this observer is quite simple
because it needs less computational effort with an additive
advantage of this observer is that its performance doesn’t
degrade with the presence of model uncertainties in the plant.
High gain observer is an asymptotic observer and dynamics of
this observer can be made arbitrarily fast through epsilon and
gains alpha’s. Separation principle theorem doesn’t need to
be proved and high gain observer can be designed separately
from the controller. The HGO is applied to multiple input and
multiple output system as:
˙x = Ax + Bϑ x, u
The HGO, then, is given by
ˆ˙x = Aˆx + Bϑ0 ˆx, u + H y − Cˆx
y = Cˆx
Where H = blockdiag H1, H2, H3, H4 ,
Hi =
∂1
1
ε
∂2
2
ε
i = 1, 2, 3, 4
ε = positive constant, and constant parameter ∂i
j are
obtained from a Hurwitz polynomial, j=1,2
s2
+ ∂1
1 s + ∂2
2 = 0
The HGO for the quadcopter system is designed in blocks
as [26] i.e six HGOs are designed for each block of equation
from (3)–(8) separately. For equation (3) HGO is implemented
as
With A =
0 1
0 0
, B =
0
1
and C =
1
0
and
ϑ0 = cos x7 sin x9 cos x11 + sin x7 sin x11
U1
m
− K1
x2
ms
and H1 is designed as in the aforementioned equation. Simi-
larly for equation (4), (5),.....(8).HGOs are given in the same
way as for equation (3).constant gains are enlisted in the table
1 in simulation section of this paper.
5

B. Sliding mode Observer with super-twisting Algorithm
One of the best sliding mode observer which offers a
finite time reaching time [27], [23] and which can be used
for sliding mode based observation is the super-twisting ob-
server.Separation principle theorem is trivial in this case too
and the Super-twisting sliding mode observer can be designed
separately from the controller. The finite time convergence
property of sliding mode observers is usually suitable in the
scheme of observation and for the purpose of observer-based
controller design for nonlinear systems Super-twisting sliding
mode observer has the form
ˆ˙x1 = ˆx2 + λ|x1 − ˆx1|1/2
sat x1 − ˆx1 (9)
ˆ˙x2 = f x1, ˆx2, u + τsat x1 − ˆx1 (10)
Taking ˜x1 = x1 − ˆx1 and ˜x2 = x2 − ˆx2 we obtain the error
equations as
˙˜x1 = ˜x2 − λ|˜x1|1/2
sat ˜x1
˙˜x2 = F t, x1, x2, ˆx2 − αsat ˜x1
where,
F x1, x2, ˆx2 = f x1, x2, u − f x1, ˆx2, u + ξ x1, x2, y
ξ is used for perturbations.For the bounded states, existence
of a constant is ensured such that
|F x1, x2, ˆx2 |< f+
Observer designed by equation (9) and (10) takes into ac-
count of partial knowledge of system dynamics while setting
parameters λ and τ and hence more accurate. The full order
Super-twisting Sliding mode observer for equation (3) is given
as
ˆ˙x1 = λ1 + ˆx1|x1 − ˆx1|1/2
sat x1 − ˆx1
ˆ˙x2 =
1
ms
cos ˆφ sin ˆθ cos ˆψ + sin ˆφ sin ˆψ U1 − K1
ˆx2
ms
τ1sat x1 − ˆx1
τ1 and λ1 are designed by the help of aforementioned in-
equality as [27]. Similarly for equations (4), (5),....., (8) Super-
twisting sliding mode observers are implemented in the same
way as for equation (3), while gains are given in the table.1
in simulation section of this paper.
V. SIMULATION STUDY
A. Closed loop Simulation with model uncertainties and with-
out noise for HGO and STSMO
Simulation results for observer-based controller of the
quadrotor are shown in the fig.3 and fig.4 for both observers
HGO and STSMO. Now under output feedback, controller
is in conjunction with HGO and STSMO separately and is
using all the states of the observer. The results in the fig.3
shows that both High gain observer and as well as Super-
twisting sliding mode observer recovers the performance of
state feedback after 12 seconds while x state a bit earlier as
compared to other states, both observers are initialized with
same initial conditions, so that performance can be compared
in a proper way and all the observers parameters are listed
in the table 1 the performance of HGO is slightly better than
STSMO in tracking as HGO estimates desired values a bit
earlier as compared to STSMO. The chattering problem is
intelligently avoided in the sliding mode control by using
continuous approximation to the sign function. This makes this
approach applicable in real applications. As the control laws
are developed for set of equations (1) but implemented on
set of equations (2) which include different kind of effects as
mentioned in section-II, similarly the model used for observer
is based on set of equations (1) and observer giving estimate
for set of equations (2) which is quadrotor model with ground
effects, drag moment, rolling moment and pitch moment.
Fig. 3. HGO and STSMO tracking of desired values
Fig. 4. HGO and STSMO tracking of desired values
B. Closed loop Simulation with noise and model Uncertainties
for HGO and STSMO
A constant noise of 0.1 value is added in the output of
the system in each of six states and the results obtained after
simulation for each observer are shown in the fig.5 and fig.6
6

which indicated that in the case of STSMO no effect on
the observer’s estimated states while on the other hand HGO
estimates deviated by the same amount as disturbance added
which opens an era of coupling integral control scheme with
HGO to eliminate the steady state error
Fig. 5. HGO Estimated values under constant sensor noises
Fig. 6. STSMO Estimated values under constant sensor noise
C. Effect of observer scheme on Control effort required
Control effort is greatly affected by the effects namely
drag moment, rolling moments, pitch moments and hub forces
which are included in the system model but not included
in any of the observer, and by the type of observer used.
Simulations results in fig.7 and fig.8 shows that HGO required
larger control effort in transient phase as compared to STSMO
and fig.9 shows that over all with the conjunction of observer
in the closed loop model control effort in transient phase in
considerably increased
Fig. 7. Control Effort with STSMO
*Initial conditions for the quadrotor system are set to zero
deliberately for evaluating performance comparison of both
observers.
Fig. 8. Control Effort with HGO
Fig. 9. Control Effort under the state feedback
TABLE I
THE NOMINAL PARAMETERS AND THE INITIAL CONDITIONS
OF THE OBSERVER AND THE SYSTEM FOR THE QUADROTOR
MODEL
Variable Value Units Initial Condition High Gain Super Twisting Sliding
Observer Mode Observer
ms 1.1 kg ˆx1(0) 1 1
l 0.21 m ˆx2(0) 2 2
Ix = Iy 1.22 Ns2/rad ˆx3(0) 0.6 0.6
Iz 2.2 Ns2/rad ˆx4(0) -2 -2
lr 0.2 Ns2/rad ˆx5(0) 2 2
K1, K2, K3 0.1 Ns/rad ˆx6(0) 1 1
K4, K5, K6 0.12 Ns/rad ˆx7(0) -1 -1
g 9.81 m/s2 ˆx8(0) 1 1
b 5 Ns2 ˆx9(0) 0.5 0.5
C 1 ˆx10(0) 1 1
k1, k3 0.8 ˆx11(0) 1.3 1.3
k2, k4 2 ˆx12(0) 3 3
k5, k7 0.5
k6, k8 5
α1, α3, α5 2
α2, α4, α6 6
zcg 0.1
4
∂1, ∂2, ∂3, ∂4 2,1,4,4
∂5, ∂6, ∂7, ∂8 6,9,10,25
ε 0.9
λ 1
τ 5
∂9, ∂10, ∂11, ∂12 2,1,6,9
VI. CONCLUSION
This paper has presented a comparison study of nonlinear
observers, including high gain observer and super-twisting
sliding mode observer in conjunction with the 2-Sliding mode
controller for the quadrotor system under external disturbances
and model uncertainties. The highgain observer can cater for
the model uncertainties but not the external disturbance while
the super-twisting sliding mode observer not only cater for
the model un-certainities but can also performs well under
external disturbances (sensor noise). The second important
result regarding initialization of high-gain observer is that it
7

doesnt allow random initialization unless gains are adjusted on
the other hand super-twisting sliding mode observer provides
flexible environment in initialization.
None of these observes is computationally onerous, but super-
twisting sliding mode observer utilizes the knowledge of
system partially [27] as compared to high-gain observer which
just rely on the Hurwitz polynomial and the tuning parameter
ε [26] so this fact also indicates the practical applicability of
super-twisting sliding mode observer in the cases where model
uncertainties are bounded as it gives finite time convergence as
compared to asymptotic convergence in the case of high gain
observer which are favourable in the environment where model
uncertainties are present or parameters are time varying in
those conditions these filters are preferable than super-twisting
sliding mode observer only. This effort will be a good starting
point to explore super-twisting sliding mode observers and to
compare them with other observers of its breed (higher order
sliding mode observers).
REFERENCES
[1] M. Chen and M. Huzmezan, “A simulation model and h (loop shaping
control of a quad rotor unmanned air vehicle.” in Modelling, Simulation,
and Optimization, 2003, pp. 320–325.
[2] S. Bouabdallah, P. Murrieri, and R. Siegwart, “Design and control
of an indoor micro quadrotor,” in Robotics and Automation, 2004.
Proceedings. ICRA’04. 2004 IEEE International Conference on, vol. 5.
IEEE, 2004, pp. 4393–4398.
[3] B. Herissé, T. Hamel, R. Mahony, and F.-X. Russotto, “Landing a vtol
unmanned aerial vehicle on a moving platform using optical flow,”
Robotics, IEEE Transactions on, vol. 28, no. 1, pp. 77–89, 2012.
[4] L. Derafa, A. Benallegue, and L. Fridman, “Super twisting control
algorithm for the attitude tracking of a four rotors uav,” Journal of the
Franklin Institute, vol. 349, no. 2, pp. 685–699, 2012.
[5] A. Tayebi and S. McGilvray, “Attitude stabilization of a vtol quadrotor
aircraft,” Control Systems Technology, IEEE Transactions on, vol. 14,
no. 3, pp. 562–571, 2006.
[6] L. Besnard, Y. B. Shtessel, and B. Landrum, “Quadrotor vehicle control
via sliding mode controller driven by sliding mode disturbance observer,”
Journal of the Franklin Institute, vol. 349, no. 2, pp. 658–684, 2012.
[7] M. Bouchoucha, S. Seghour, and M. Tadjine, “Classical and second
order sliding mode control solution to an attitude stabilization of a four
rotors helicopter: From theory to experiment,” in Mechatronics (ICM),
2011 IEEE International Conference on. IEEE, 2011, pp. 162–169.
[8] E. Altu˘g, J. P. Ostrowski, and R. Mahony, “Control of a quadrotor
helicopter using visual feedback,” in Robotics and Automation, 2002.
Proceedings. ICRA’02. IEEE International Conference on, vol. 1. IEEE,
2002, pp. 72–77.
[9] E. Altu˘g, J. P. Ostrowski, and C. J. Taylor, “Quadrotor control using
dual camera visual feedback,” in Robotics and Automation, 2003.
Proceedings. ICRA’03. IEEE International Conference on, vol. 3. IEEE,
2003, pp. 4294–4299.
[10] T. Madani and A. Benallegue, “Control of a quadrotor mini-helicopter
via full state backstepping technique,” in Decision and Control, 2006
45th IEEE Conference on. IEEE, 2006, pp. 1515–1520.
[11] ——, “Backstepping sliding mode control applied to a miniature quadro-
tor flying robot,” in IEEE Industrial Electronics, IECON 2006-32nd
Annual Conference on. IEEE, 2006, pp. 700–705.
[12] P. Castillo, P. Albertos, P. Garcia, and R. Lozano, “Simple real-time
attitude stabilization of a quad-rotor aircraft with bounded signals,” in
Decision and Control, 2006 45th IEEE Conference on. IEEE, 2006,
pp. 1533–1538.
[13] N. Metni, T. Hamel, and F. Derkx, “Visual tracking control of aerial
robotic systems with adaptive depth estimation,” in Decision and Con-
trol, 2005 and 2005 European Control Conference. CDC-ECC’05. 44th
IEEE Conference on. IEEE, 2005, pp. 6078–6084.
[14] A. Benallegue, A. Mokhtari, and L. Fridman, “Feedback linearization
and high order sliding mode observer for a quadrotor uav,” in Variable
Structure Systems, 2006. VSS’06. International Workshop on. IEEE,
2006, pp. 365–372.
[15] D. G. Luenberger, “Observers for multivariable systems,” Automatic
Control, IEEE Transactions on, vol. 11, no. 2, pp. 190–197, 1966.
[16] F. Esfandiari and H. K. Khalil, “Output feedback stabilization of fully
linearizable systems,” International Journal of control, vol. 56, no. 5,
pp. 1007–1037, 1992.
[17] A. N. Atassi and H. K. Khalil, “A separation principle for the stabiliza-
tion of a class of nonlinear systems,” IEEE Transactions on Automatic
Control, vol. 44, no. 9, pp. 1672–1687, 1999.
[18] A. Isidori, “A remark on the problem of semiglobal nonlinear output
regulation,” IEEE transactions on Automatic Control, vol. 42, no. 12,
pp. 1734–1738, 1997.
[19] Z. Lin and A. Saberi, “Robust semiglobal stabilization of minimum-
phase input-output linearizable systems via partial state and output
feedback,” Automatic Control, IEEE Transactions on, vol. 40, no. 6,
pp. 1029–1041, 1995.
[20] W. J. Rugh, Linear system theory. prentice hall Upper Saddle River,
NJ, 1996, vol. 2.
[21] J. Barbot, M. Djemai, and T. Boukhobza, “Sliding mode observers,”
Sliding Mode Control in Engineering, vol. 11, 2002.
[22] C. Edwards, S. K. Spurgeon, and C. P. Tan, “On the development and
application of sliding mode observers,” in Variable Structure Systems:
Towards the 21st Century. Springer, 2002, pp. 253–282.
[23] J. Davila, L. Fridman, and A. Levant, “Second-order sliding-mode
observer for mechanical systems,” IEEE transactions on automatic
control, vol. 50, no. 11, pp. 1785–1789, 2005.
[24] M. Becker, R. C. B. Sampaio, S. Bouabdallah, V. d. Perrot, and
R. Siegwart, “In-flight collision avoidance controller based only on
os4 embedded sensors,” Journal of the Brazilian Society of Mechanical
Sciences and Engineering, vol. 34, no. 3, pp. 294–307, 2012.
[25] I. Khan, A. Bhatti, Q. Khan, and Q. Ahmad, “Sliding mode control
of lateral dynamics of an auv,” in Applied Sciences and Technology
(IBCAST), 2012 9th International Bhurban Conference on. IEEE, 2012,
pp. 27–31.
[26] H. K. Khalil and J. Grizzle, Nonlinear systems. Prentice hall New
Jersey, 1996, vol. 3.
[27] Y. Shtessel, C. Edwards, L. Fridman, and A. Levant, Sliding mode
control and observation. Springer, 2014.
8

Fuzzy Temperature Controller for Induction
Heating
Bushra Aijaz
Dept. of Electrical Engineering
Bahria University
Karachi, Pakistan
bushra.aijaz@bimcs.edu.pk
Rahema Kaleem
Dept. of Electronic Engineering
NED University of Engg & Tech
Karachi, Pakistan
rahemakaleem@gmail.com
Naeema Saeed
Dept. of Electronic Engineering
NED University of Engg & Tech
Karachi, Pakistan
naeemasaeed991@hotmail.com
Abstract— this paper focuses on Fuzzy Temperature Controller
(FTC) for induction heating, which aims to provide a precise and
intelligent temperature controller. Induction heating is a non
contact process and so it is safer. The heating all depends on the
larger number of Cu and Eddy losses and thus faster. Variable
frequency Inverter is used to achieve temperature control for
induction heating coils. By controlling the output frequency of
inverter, the temperature of the load is controlled. Fuzzy logic is
implemented for overall controlling. FTC is modelled on DSP
Starter Kit DSKC6713. All programming is written in C-
language. The model of this idea has been designed and tested
using LabVIEW 8.5.
Keywords – Fuzzy Logic, Induction Heating, DSP, Variable
Frequency Inverter.
I. INTRODUCTION
Precise temperature controlling and fast heating processes
are integral part of industries. The industries require a new
modern technique which can handle temperature controlling
with more accuracy and precision as the controllers currently
used are slow and inaccurate plus they are unsuitable for non-
linear measurements.
The block diagram of the system is shown in Figure 1
Project Block Diagram.
Figure 1 Project Block Diagram
The system consists of DSK DSPC6713 for generating
SPWM pulses, isolation circuit inverter, transformer (step-up)
and temperature sensing circuit. SPWM pulses are fed to gate
drivers and then to inverter. The output is then fed to step up
transformer to obtain the desired output level which is then
fed to the load for heating. Controlling of temperature is done
by controlling the operating frequency of inverter.
The rest of the paper is organized as follows: in Section II, we
describe the inverter in brief. Fuzzy Logic Controller is
described in Section III, SPWM is generated in Section IV,
Temperature Sensors are interfaced in section V. Induction
Heating is discussed in Section VI and the paper is concluded
in Section VII.
II. INVERTER
This section focuses on DC to AC inverter. The purpose is to
efficiently convert a DC power source to a high voltage AC
source, which will be required to drive the load. This is
achieved by first converting low voltage DC power source to
high voltage DC power source and then the HV DC power to
AC power source using sinusoidal pulse width modulation
technique, the output of which is 220Vac.
As the induction coils operate at much higher frequencies so a
high frequency inverter is needed. To accomplish this, the
converter is designed using a full-wave rectifier. Smoothening
capacitors are connected at the output of the full-wave
rectifier to convert rectified pulsated output to smooth DC
output voltage. This output is then given to H-bridge that
gives AC square wave of 220V. The circuit and output is
shown in Figure 2.
9

Figure 2 Design and Simulation of H- Bridge
To minimize the power loss and to ensure high switching
speeds, N-channel MOSFETs IRF840 are used in H- Bridge.
MOSFET gate drivers are used and controlled through SPWM
signals coming from the DSPC6713. The values for
bootstrapping capacitors and diodes are calculated using
Equation 1.
The output of H-Bridge gives 220 Vac whose frequency can be
varied to more than 200Hz.
III. FUZZY LOGIC CONTROLLER
Fuzzy logic is doing all sort of controlling. The logic was
first proposed by Zadeh in 1965.
The Fuzzy logic is a linguistic logic. It is similar to crisp
logic but with the difference is that crisp logic has only two
levels of decision either 0 or 1 but fuzzy logic can have levels
in between them, which makes the logic precise and close to
ideal behaviour.
The fuzzy structure is based on following three steps;
A. Pre-processing
As shown above; Error and rate-of-error are the inputs to
the fuzzy controller, whereas error is the difference of set
temperature and Feedback temperature. The inputs are
properly assigned with their membership (µ) functions (after
observing temperature rise and fall graph described in section
V). Figure 3 and Figure 4 shows the µ function assignments of
the two inputs ΔT and ΔT/Δt and Figure 5 shows the µ
functions of output ΔF.
Figure 3Membership functions for input Fuzzy variable ΔT
Figure 4 Membership functions for input Fuzzy variable ΔT/Δt
Figure 5 Membership functions for output Fuzzy variable ΔF
B. Fuzzy Inference
A set of rules is defined for controlling purpose. In this
project, Fuzzy logic consists of 5 levels of decision for both
the inputs and output. So the rule set comes up with 25 levels.
The Table 1demonstrates the set of rules for ΔF.
Table 1 Fuzzy Rule Base
Delta T
Delta (T/t)
NB NS Z PS PB
NB NB NB NB NS Z
NS NB NB NS Z PS
Z NB NS Z PS PB
PS NS Z PS PB PB
PB Z PS PB PB PB
C. Defuzzification
After evaluating the rule(s), the system comes up with a
certain output frequency (ΔF) which is then fed to DSP
controller. The ΔF tells the inverter how much (variable)
frequency it has to produce for required heating. Figure 6
shows the output ΔF for two inputs ΔT and ΔT/Δt.
ΔT
ΔT/Δt
Fuzzy
Controller ΔF
10

Figure 6 simulation output for given input sample values
IV.SPWM GENERATION
SPWM pulses are constant amplitude pulses with different
duty cycles for each period. For SPWM generation reference
signal is compared with carrier (triangular wave). A sinusoidal
waveform signal is used as a reference signal to shape the
output (AC voltage) close to sinusoidal.
Figure 7 shows the comparator circuit for SPWM
generation.
Figure 7 Comparator for SPWM generation
For real time frequency variation of SPWM signals, variable
frequency sine wave is created. For sampling frequency of
8KHz, frequency of generated sine wave, Equation 2 is used,
Where n represents number of points inputted for sine wave
generation.
With the change in value of ‘n’, the frequency of sinusoidal
wave gets changed. Figure 8 shows the output behaviour of
Variable frequency sine wave generated.
Figure 8 Output of Variable frequency sine wave
SPWM signals are generated by comparing triangular wave
(1KHz) with variable frequency sinusoidal wave. The
amplitude modulation ratio is set to 0.9 and frequency
modulation ratio varies as frequency of reference signal
varies, determined by Equation 3;
Figure 9 shows the successful generation of SPWM wave on
CCS graph.
Figure 9 SPWM generation on CCS graph
V. TEMPERATURE SENSOR INTERFACING
Temperature detectors are necessary element in order to
carry out the temperature controlling. The output of sensor is
connected to DAQ (NI-PXI-6229) to link the sensor with the
software environment.
This output of DAQ assistant is multiplied with sensor
sensitivity. The output of multiplier block is fed to formula
block and collector block. The formula block is converting the
analog signal into o
Centigrade and the collector block
produces the mean of collected readings. The collector output
is fed to signal conditioning block. The readings finally
coming out are being written to measurement file block. The
whole circuit is enclosed in a while loop to carry out the
reading process continuously as shown in Figure 10.
Figure 10 VI window for Temperature Sensor Interfacing
Circuit
The temperature is forced to bring around 70 to 75o
C. The
change in temperature is noted down in the “write to
measurement file”. This data is plotted and the rise and fall
response of temperature is modelled as shown in Figure 11
and Figure 12 below:
Figure 11 sensor response when temperature rises
V8
TD =
TF = .00002775
PW = 5.55e-7
PER = 5.55e-5
V1 = 5v
TR = 0.00002775
V2 = -5v
U1
OPAMP
+
-
OUT
0
5.000V0
0V
R1
1k
0V
V2
FREQ = 1khz
VAMPL = 4v
VOFF = 0v
V
0
11

Figure 12 sensor response when temperature falls
VI.INDUCTION HEATING
Induction heating has replaced the traditional furnace
methods because of its efficiency, unpolluted and fast heating
process. Electrically conductive materials are used in
induction heating for heating purposes and this requires high
frequency electricity. An IH system requires a source of
alternating current, an induction coil, and the work piece to be
heated. A magnetic field is generated in the coil due to the
alternating current passing through the coil. The AC is
supplied by the inverter. Work piece placed within the coil
will experienced the magnetic field due to which eddy
currents are induced in the work piece that cause non-contact
type of heating between work piece and the induction coil.
Copper tube is used to make induction coil. The tube is
hollow inside with coil diameter about 3 inches and internal
diameter of tube is about 1cm.the coil is given 8 turns. The
impedance of coil depends on cross-sectional area, length and
the number of turns so to increase the coil impedance.
The impedance matching circuit is designed such that it
converts high volt/low current (coming out from inverter) to
low volt/high current (driven requirement of the load). The
choice is made for impedance matching in order to match the
output parameters of inverter with the input parameter of coil.
Parallel capacitor bank is to be connected between the coil and
inverter.
VII. CONCLUSION
The FLC for induction heating has been presented in this
paper. The integrated controller, implemented on DSKC6713
takes the set temperature (required temperature of load) input
from user, reads actual temperature of load and calculates
error and rate of error, then on basis of Fuzzy Logic, it takes
decision and determines the amount of ΔF that needs to be
added/subtracted. The FLC then feeds this ΔF to/from
operating frequency of variable frequency inverter.
The FL is being implemented in international industries but
is new for Pakistani industries. However, Pakistan industries
are doing their controlling on fuzzy logic but it is a joint
venture of PID and FUZZY. This idea, however, introduces
Fuzzy Temperature Controller as a standalone product.
BIBLIOGRAPHY
[1] Chin-Hsing Cheng, “Design of Fuzzy Controller for Induction
Heating Using DSP”, 5th
IEEE Conference on Industrial Electronics
and Applications, 2010
[2] Yunseop Kim, “Fuzzy Logic Temperature Controller”, Physics
344 project, 2001
[3] Datasheet IRF840:
http://www.datasheetcatalog.com/datasheets_pdf/I/R/F/8/IRF840.sht
ml
[4] HV Floating MOS-Gate Driver ICs, Application Note AN-978,
http://www.irf.com/techincal-info/appnotes/an-978.pdf
[5] Zememe Walle Mekonnen, “Digital Signal Processing
Applications using C6713 DSK”, project work
[6] S. Zinn, S.L Semiatin, “Elements of Induction Heating, Design,
Control and application”
12

Stability and Control Solution to Quad-copters
Q. Ejaz Ur Rehman1
, S.Akhtar1
, A.Saleem1
Department of Avionics Engineering1
National University of Sciences and Technology
Islamabad, Pakistan
qejaz@cae.nust.edu.pk
Suhail@cae.nust.edu.pk
ammar.saleem@cae.nust.edu.pk
Abstract ‒ A Quad-copter is a structurally simple and
dynamically complex rotorcraft, lifted and propelled
by four rotors. It has very small size and is highly
maneuverable as compared to conventional
helicopters. In this paper, a method to achieve control
and stability of a quad-copter is presented.
Computational tools employed are
MATLAB®
/Simulink®
, Catia®
, LabVIEW®
, ANSYS®
and Arduino IDE®
. A Mathematical model of quad-
copter dynamics is developed using set of derived
nonlinear equations accompanied by control theory.
This nonlinear mathematical model is linearized in
Matlab and LabVIEW®
. Linear equations are used to
design Linear Quadratic Regulator (LQR) controller.
Microcontroller and sensor used are Arduino Mega
2560 and 6 DoF1
IMU2
. Stability of quad-copter is
validated through experiments and simulations.
Keywords: Quad-copter, Stability, Controllability,
LQR, Mathematical Modeling, IMU
I. INTRODUCTION
In recent years, the aeronautical industry has shown a
growing interest in UAVs (Unmanned Aerial
Vehicles). UAVs are growing in popularity in fields of
medicine, engineering, civil and most importantly,
military and security. The reduced cost, absence of a
trained pilot and small compact size make them viable
options for tasks that include inhospitable terrain and
remote regions.
Quad-copters are the conventional remote control
airborne vehicles with four rotors placed at equivalent
distance from center of gravity. Quad-copter is
elevated and driven by these four rotors only. Quad-
copters are structurally simple and unstable unmanned
air vehicles (UAVs). Due to its simple structure it is
popular UAV nowadays, being used for surveillance,
1
Degree of Freedom
2
Inertial Measurement Unit
aerial photography, Bomb search and disposal, vision
based pose estimation and Fertilizer/ Pesticide sprayer
etc.
Unlike most helicopters, Quad-copters use two sets of
indistinguishable static level propellers (two
clockwise (CW) and two counter-clockwise (CCW))
which are set in an X or + (plus) configuration with X
being the preferred configuration as shown in figure 1.
These use deviation of RPM to control thrust and
torque. Roll, Pitch and Yaw of quad-copter is achieved
by altering the rotation rate of one or more rotor discs,
thereby changing its torque load and thrust/lift
characteristics.
Figure 1: Rotors 1 and 3 rotate in one direction, while
rotors 2 and 4 rotate in the opposite direction,
controlling opposing torques for controllability and
stability.
The dynamics linked to employing four rotors
mounted on edges of a square shape create a highly
unstable platform that can only be controlled by
embedding complex algorithms onboard.
Due to the dynamically unstable nature of rotors,
complex control mechanisms are required for a
sustained flight.
13

In this paper, a method to achieve controllability and
stability of quad-copter at certain height is achieved
such that it is stationery with respect to the earth frame
of reference at certain height. Simulation platform
used are MATLAB®
and LabVIEW®
, while detail
study of quad-copter and propeller is conducted in
ANSYS-FLUENT®
. CAD models are modelled in
CATIA®
software. The algorithm is written by
manipulating the non-linear differential equations with
control theory. The algorithm written is verified by
visualizing results, animations and virtual reality
model to completely study the quad-copter behaviour
and response to inputs.
The algorithm is translated into equivalent C language
for Hardware testing. Microcontroller and sensor used
are Arduino Mega 2560 and 6-DoF Razor IMU only.
II. EQUATION OF MOTION
The governing equations for the control of quad-copter
are derived in this section. First of all, translational and
rotational dynamics of quadrotor are explained
followed by simplifications. Bold symbols are used to
denote three-dimensional vectors, while non-boldface
symbol are used for scalars in the paper.
Figure 2: (A) Dynamic model of a quad-copter with
four propellers in Earth frame of reference (B)
Propeller i producing fi thrust with 𝜔𝑖 rpm in z-
direction
A. Dynamics
A quad-copter is a UAV having four rotors and a mass
‘m’. The forces which act on a quadrotor are its weight
and the thrust f produced by four propellers in body
fixed direction z = (0, 0, 1). Similarly, four torques acts
on each propeller and a total drag torque acts on quad-
copter body. The rotation of the body fixed frame with
respect to some inertial frame is described by the
rotation matrix R which is discussed in detail.
Two coordinate systems are considered in Figure 4
[3]:
 The inertial frame (E-frame)
 The body-fixed frame (B-frame)
Figure 3: Quad-copter Frame of References
These are related through three rotations:
 Roll: Rotation of φ around the x-axis;
 Pitch: Rotation of θ around the y-axis;
 Yaw: Rotation of ψ around the z-axis.
The following assumptions were made in this
approach:
 The body-fixed frame origin and center of
mass (COM) of the body of the vehicle are
coincident.
 The axes of the B-frame coincide with the
body principal axes of inertia.
Figure 4: Quad-copter configuration frame
system
Given equation describes the relation of Rotation
matrix with roll, pitch, yaw and quad-copter position
in earth frame.
R (,, )  R(x,) R(y,) R (z, ) ( 1 )
The main equation governing the quad-copter
dynamics is:
14

m𝐝̈ = 𝐑𝐳 ∑ fi +4
i=1 m𝐠 ( 2 )
Where d= (d1, d2, d3) are position vectors in inertial
frame of reference and g = (0, 0, -9.81) is gravitational
constant.
As quad-copter is a rigid body with four propellers.
The body inertia is expressed as diagonal matrix
IB
= [
Ixx
B
0 0
0 Iyy
B
0
0 0 Izz
B
] ( 3 )
The propeller is a symmetric body with respect to its
axis of rotation and can be considered as disc for
simplification. The propeller inertia is given by:
IP
= [
Ixx
P
0 0
0 Iyy
P
0
0 0 Izz
P
] ( 4 )
Due to symmetry of propeller Ixx
P
= Iyy
P
.
The angular velocity of a body can be governed using
the differential equation: [2]
τres = τB + ∑ τP + ⟦ωB
× ⟧(LB +4
i=1
∑ IP
ωB
+ ∑ LP
4
i=1
4
i=1 ) ( 5 )
Where τres is the resultant torque acting on quad-
copter body. τB is the body torque
τB = (Ixx
B
ṗ, Iyy
B
q̇, Izz
B
ṙ) ( 6 )
And τP is the torque produced by propeller.
τP = (0, 0, Izz
P
ωi̇ ) ( 7 )
B. Simplification of Assumptions
The effect of all the moments acting upon the body is
denoted by τres on the right hand side of equation (5).
These include moments due to propeller forces and
torques due to motors. The propeller forces are
assumed to act through the center of each propeller. It
is assumed that the center of propeller is at a horizontal
distance l from the body center of mass.
τres = [
(f2 − f4)l + τdx
(f3 − f1)l + τdy
τ1 + τ2 + τ3 + τ4 + τdz
] ( 8 )
With τd = (τdx
, τdy
, τdz
) is the drag torque. It has
been observed that propellers reaction torque has a
linear relation with the thrust force (with
proportionality constant of kτ and sign given by the
sense of rotation).
τi = (−1)i+1
κτfi ( 9 )
And thrust is related to rotor rpm as
fi = κfωi
2
( 10 )
Solving the above equation from 3 to 8 results in [2]
Ixx
B
ṗ = κf(w2
2
− w4
2)l − (Izz
T
− Ixx
T )qr − Izz
P
q(w1 +
w2 + w3 + w4), ( 11 )
Ixx
B
q̇ = κf(w3
2
− w1
2)l + (Izz
T
− Ixx
T )pr + Izz
P
p(w1 +
w2 + w3 + w4), ( 12 )
Izz
B
ṙ = −γr + κτκf(w1
2
− w2
2
+ w3
2
− w4
2
) ( 13 )
III. CONTROLLABILITY
These non-linear equations are linearized to compute
state space matrices A, B, C and D. An LQR controller
was designed with the cost value of 0.5 s2
rad-2
on the
angular rates, 10 on the deviation from the primary
axis, 0 on the extended motor states and 0.75 N-2
on
the inputs.
IV. EXPERIMENTAL PLATFORM
Computational Programming softwares employed
were MATLAB®
/ SIMULINK®
, LabVIEW®
,
ANSYS®
, Arduino IDE®
and CATIA®
.
CAD models of Quad-copter and equivalent propeller
were modelled in CATIA®
and were exported to
ANSYS-FLUENT®
where surface meshing and
computational fluid dynamics (CFD) was done to
study aerodynamic design such that fluid was passed
on to the quad-copter and propeller at different speeds
and direction to check its serviceability.
The propeller produced vibrations which were verified
from CFD analysis. These vibrations were catered
using prop-balancer system.
15

V. MODELLING OF PROPELLER
A. Actuator Disk Theory
A propeller can be represented as a single disk
operating in a stream tube. As the flow passes through
the Disk its velocity decreases while pressure
increases. The disk is infinitely thin but has an area.
Here propeller acts like it is made up of infinite blades.
This disk produces pressure jump across it which is
equal to thrust per unit area of disk.
B. Geometry of Fan
In order to model the propeller a thin circular
surface of area equal to swept area of propeller was
created around the hub. This thin surface was enclosed
in a disk shaped volume such that the diameter of disk
and thin surface was same. Two fluids, fluid fan 1 and
fluid fan 2 were defined on both sides of thin in
between thin and disk surface.
Figure 7: Thin enclosed in disk
Figure 8: Volume Mesh with Quality
Figure 9: Vectors through thin
The inertia of the quad-copter was measured by
suspending the quad-copter about 3 different axis and
measuring its period of oscillation. The inertia is given
by:
I =
k
(2πT)2 ( 14 )
Where k is the torsion constant and T is the period of
oscillation of quad-copter with reference to
Figure 5: Actuator Disk in flow stream
Figure 6: Thin with fluid fan
16

equilibrium position. Due to quad-copter
symmetry Ixx
P
= Iyy
P
.
The propeller inertia was approximately measured by
considering propeller as disc and motor as cylinder. As
propellers are fixed in zth
direction so, Ixx
P
= Iyy
P
= 0
The quad-copter’s mass was determined to be 1 Kg.
The distance from the center of gravity of the quad-
copter to center of propeller was 0.24m. The propeller
reaction torque and force constant were estimated as
κτ = 0.214272
Nm
N
and κf = 1.80899e − 5
Ns2
rad2.
VI. VOICE CONTROL PANEL
A simulation based voice control panel was also
developed in LabVIEW®
to control quad-copter in 6-
DoF which is shown in Figure 8.
It operates on the certain commands and performs
tasks as per the commands which are embedded in the
algorithm.
Figure 10: Voice Command Panel in LabVIEW®
for
quad-copter
VII. Mathematical Model
A mathematical model was developed in LabVIEW®
and MATLAB®
using nonlinear equations discussed
in section II. Implemented nonlinear equations were
linearized to design LQR controller for feedback
control. Implemented mathematical model, results and
animation is shown:
Figure 11: Non-Linear Mathematical Model
17

Figure 12: Simulation Results
From figure 12 it can be seen p, q, r (angular rates
(degs/s) about x, y and z axis, respectively) showed
variation at the start of quad-copter but came to
equilibrium position at t=2, 5, 3 sec respectively.
Subsequently roll, pitch and yaw angle also settled
after 5 sec of takeoff. While linear velocities U and W
about x and z axis rises continuously and linear
velocity V about y-axis comes to equilibrium position
after 10 sec which depicts the quad-copter has gained
height of 10 ft. above ground as justified from X, Y, Z
in the figure 10.
Figure 13: Throttle and RPM relation with time
Figure 13 shows the variation of produced thrust from
throttle given at certain time t for each rotor.
Figure 14: Animation GUI of Hover Model
An animation GUI has also been implemented which
shows the behavior of quad-copter and also display the
trajectory made by quad-copter to reach height of 10
ft.
Figure 15: Virtual Reality Quad-copter Model
A quad-copter model has also been made using
Matlab® virtual reality toolbox in order to make the
simulations near to reality. This virtual model take
roll, pitch, yaw and rotor forces as input and depicts
the result of the mathematical model. It was stabilizing
itself after takeoff, initially showing some small
vibrations which can also be visualized from figure 10.
VIII. CONCLUSION
Quad-copter is a highly unstable UAV, but due to its
high maneuverability, it is highly desired for field
works. Utilizing nonlinear dynamic equations
accompanied with control theory can bring quad-
copter to life. These algorithms sufficiently reduce the
need of pilot and can be used to build cheap UAVs
which can reduce cost to a great extent. Quad-copter
are the future robots in this field in particular as well
as in other fields in general, being applied alongside
18

people which will help in making tasks easier and
more efficient. Developed mathematical model was
successfully implemented on hardware.
IX. ACKNOWLEDGMENT
This work has been made possible by the help of my
co-authors which include my project advisor and co-
advisor. This text has been rectified and proof read by
Undergraduate students M. Moghees Shahid and Ali
Mahmood. They are destined for great things.
This project has been sanctioned by College of
Aeronautical Engineering, NUST.
REFERENCES
[1] T. Luukkonen, "Modelling and control of
quad-copter," Independent research project
in applied mathematics, Espoo, 2011.
[2] M. W. Mueller and R. D'Andrea, "Stability
and control of a quadrocopter despite the
complete loss of one, two, or three
propellers," in Robotics and Automation
(ICRA), 2014 IEEE International Conference
on, 2014, pp. 45-52.
[3] I. Gaponov and A. Razinkova, "Quad-copter
design and implementation as a
multidisciplinary engineering course," in
Teaching, Assessment and Learning for
Engineering (TALE), 2012 IEEE
International Conference on, 2012, pp. H2B-
16-H2B-19.
[4] D. Hanafi, M. Qetkeaw, R. Ghazali, M. Than,
W. Utomo and R. Omar, 'Simple GUI
Wireless Controller of Quad-copter',
International Journal of Communications,
Network and System Sciences, vol. 06, no.
01, pp. 52-59, 2013
[5] Y. Cooper, R. Ganesh Ram, V. Kalaichelvi
and V. Bhatia, 'Stabilization and Control of
an Autonomous Quad-copter', AMM, vol.
666, pp. 161-165, 2014.
19

Detection of Fire Hotspots dealt by Emergency
First Responders in Rawalpindi using GIS
applications
Amna Butt and Sheikh Saeed Ahmad
Department of Environmental Sciences
Fatima Jinnah Women University
Rawalpindi, Pakistan
ambutt91@yahoo.com; drsaeed@fjwu.edu.pk
Abstract— During the past, need of efficient Emergency First
Response (EFR) for Rawalpindi, along with all the major cities of
Pakistan has increased tremendously. Therefore, there is a need
to develop an effective management strategy to improve the first
response services. Present study focused on the identification of
past and current service locations for fire incidences and
mapping these locations for hotspot identification. The incidence
data for past five years (2009-13) was collected and Hotspot and
Spatial Autocorrelation analyses were performed on the data to
detect the fire hotspots and their clustering patterns in the city.
The results revealed a slight shift in fire hotspots in 5 years and
also in the clustering pattern which changed from significantly
clustered (2009) to randomly distributed (2013). Hotspot and
spatial distribution maps were generated to indicate the fire
hotspots in the city. These maps can be helpful to prevent the
future incidents by allocating more service stations focusing these
areas for fire mitigation.
Index Terms— Hotspot Analysis, Fire, Kernel Density,
Emergency First Response (EFR), Spatial Autocorrelation
Analysis
I. INTRODUCTION
Coping with fire, caused either by natural or anthropogenic
factors is one of the challenges faced by the modern societies
[1]. Analyzing the city fire risk is therefore highly significant
for development of effective urban fire protection plan and
regulations and facilitates the coordinated development of
social economy [2].
Application of geostatistical tools of GIS can play a
significant role in improvement of local fire emergency
response [3] primarily by facilitating the visualization and
interpretation of nature and previously observed patterns of
such accidents [4][5]. Generating different fire risk maps on the
basis of geostatistical analysis is also imperative to develop
strategies focused on alleviating the future risk [6].
Numerous approaches based on GIS have been developed
and used over the past to provide geostatistical surveillance of
the precedent emergency patterns for development of several
models for fast and apt response delivery [7][8][9][10][11].
A. Study Area
The study area of the present research was Rawalpindi city
(Fig. 1). The city’s administrative boundaries consist of two
tehsils namely Rawal and Potohar tehsil. Currently five service
stations and two key points of Rescue 1122 (otherwise known
as Punjab emergency service) are providing emergency
response services (including fire brigade service) in different
areas of the city. The resources currently available to them for
providing Fire brigade service include 9 fire vehicles, 14
ambulances and personnel of 400 trained rescue providers.
However, no prior study has been conducted in the city focused
on surveillance of fire emergency response. Furthermore, the
existing management strategy for improvement of fire
emergency response is not very effective and no thought has
been given to incorporating GIS expertise in the department for
this purpose.
Fig. 1. Study Area map: Rawalpindi City
20

Therefore, the primary objective of the present research is
to provide a GIS based surveillance system for the Fire
incidents of the past in order to determine the recurrent service
locations for focusing the future response. The study can
therefore be considered as a baseline for future improvement in
the quality and efficiency of fire emergency responses in
Rawalpindi city.
II. MATERIALS AND METHODS
The research methodology that was applied to obtain the
results primarily consisted of four main steps incorporating
data collection, processing, analyzing and visualizing the
results (Fig.1).
Fig. 2. Flowchart of main steps followed in methodology
B. Data Collection and Processing
The data collected for the purpose of this study was divided
into two categories namely Primary and Secondary data. The
main data obtained for the purpose of the study was secondary
data acquired from the headquarters of Rescue 1122,
Rawalpindi in form of caller and victim directory. This data
was collected on unit level i.e. data from all the emergency
units of 1122 at work in Rawalpindi city was acquired,
compiled and then processed for segregation of Fire cases.
Primary data was then collected on the basis of segregated fire
incidents. This data comprised the GPS readings of incident
locations obtained via handheld Oregon 650 GPS for the
reported fire cases.
Both primary and secondary data was processed in
Microsoft excel and then loaded to ArcGIS 10.2 for further
processing and analyzing.
C. Geostatistical Analysis of data
The data was geostatistically analyzed in ArcGIS 10.2
environment for determination of spatial clustering and
identification of hotspots. The geostatistical analysis performed
for this purpose included Global Moran’s I test (Spatial
autocorrelation analysis) and Hotspot Analysis (Getis-Ord
Gi*).
1) Global Moran’s I statistics
Global Moran’s I statistic gives an indication of any
existing correlation among spatial observations and delineates
the characteristics of the global pattern. The pattern maybe
random, dispersed or clustered depending on the spatial
association present in the data [12][13]. For the purpose of
present study spatial autocorrelation among the fire incidents
was calculated on yearly basis by employing different
threshold limits ranging from 500-2000 meters. The range used
for determination of correlation was -1 to 1 and Z-score value
was calculated to assess the statistical significance of the
observed clustering (based on correlation) for each year. The
highest correlation values were then recorded for each year and
subsequently were employed for hotspot identification.
2) Hotspot Analysis: Getis-Ord Gi*
Fire hotspots were identified based on the Getis-Ord Gi*
statistics. For this purpose, the conceptualization of spatial
relationship among different datasets was done by opting
“Zone of indifference”. The threshold limit was set on the basis
of spatial autocorrelation outcomes for each year (exhibiting
highest Z-score value). Thereafter, the identified hotspots for
Fire cases were interpolated using “Inverse Distance
Weighted” or “IDW” for better visualization of results and
hotspot maps for each year were generated.
III. RESULTS
The emergency callout data on building fires, bursting of
gas pipes, cylinder blasts, and gas leakages was cataloged in
the category of Fire emergencies. Different geostatistical
analyses were then performed to determine the pattern of
emergency cases for the study duration. The reported incidence
of FE cases for the time period of 2009-2013 were 671. Out of
which, 583 (86.9%) were males and 88 (13.1%) females. 15%
of the total fire incidents (102 cases) were reported in 2009 and
37% (247 cases), the highest incidence, in 2010. After 2010 the
incidence rate declined progressively from 24% (163 cases) in
2011 to 10% (66 cases) in 2012 and subsequently rose to 14%
(93 cases) in 2013 (Fig. 3).
Fig. 3. Percentage Contribution of Fire Incidents in Rawalpindi during 2009-
2013
The possible spatial autocorrelation of Fire cases estimated
by Moran’s I statistics revealed significant spatial clustering for
the years 2009 and 2011, whereas 2010 showed mild
clustering. 2012 and 2013 on the other hand showed random
patterns. Moran’s I and G-statistic values (Z-score) for all the
years, given in Table 1 disclosed that 2009 had highest (20.01)
while 2013 had lowest (1.10) Z-score.
21

Table 1. Global spatial autocorrelation statistics of Fire emergencies for 2009-
2013
Year Moran's I Z Score P Value Pattern
2009 0.77574 20.0117 0.001000 Clustered
2010 0.01719 2.09997 0.035732 Clustered
2011 0.17427 4.3162 0.000016 Clustered
2012 0.39961 1.51482 0.129818 Random
2013 0.02737 1.10479 0.269251 Random
Local Gi* (d) statistic employed to identify the hotspots for
Fire incidents in Rawalpindi during 2009-2013 categorized the
Z-score outcomes at 5% significance level as either clusters or
non-clusters. The identified Fire hotspots covered both urban
and rural areas of Rawalpindi city for all the years. The specific
hotspot locations for each year are tabulated in Table 2
however.
Table 2. Identified Fire Hotspots for the years 2009-2013
Year Identified Hotspot Locations
2009 Asghar Mall Chowk, Banni Chowk, Chaklala Scheme 3, Chandni
Chowk, Islamabad Highway, Khanna Pull, Link Road, Murree Road,
Muslim Town, Naz Cinema Chowk, Rehmanabad, Sadiqabad
Chowk, Transformer Chowk and Waris Khan Stop
2010 A.R.I.D University road, Band Khanna Road, Bilal Hospital road,
Dhok Kashmiriyan, Double Road, Faizabad, Ghosia Chowk, Iqbal
Town, IJP road, Kattariyan, Khayaban-e-Sirsyed, Kurri road, Rabi
Center, Saidpur road Satellite Town, Sixth road, Shamsabad and
Sohan Pull
2011 Dhok Mustakeem, Choor Chowk, Golra Morr, Misriyal road,
Peerwadhai Morr, Qasimabad, Seham road and Westridge
2012 Askari 11, Faisal Colony, Jhanda Chichi, Military Hospital Road,
Peshawar Morr, Pindora Chungi and Shamsabad Stop
2013 Adyala road, Chaklala Scheme 3, Committee Chowk, Khanna Pull,
Link road, Raja Bazar, and Rawal road
Figure 4 revealed that Fire hotspots for 2009 and 2010 were
mostly contained in the Northern region of Rawal Tehsil and
shifted towards North-West in 2011. However, during 2012
and 2013 not only the number of hotspots reduced significantly
but the spatial distribution pattern also became random.
Fig. 4. Mapping of Fire hotspots using Getis-Ord G* statistics during 2009-
2013
Spatial distribution of Fire cases in hotspot locations was
also visualized by creating spatial distribution maps (Fig. 5).
The highest incidence (based on the number of cases per
locale) was observed in 2010 while the lowest was observed in
2012. The distribution pattern revealed that the highest
incidence was mainly in urban areas of Rawal tehsil and North-
East portion of Potohar tehsil where the reported cases per
locality per year were as high as 10.
22

Fig. 5. Spatial Distribution pattern of Fire incidents in Rawalpindi for study
duration
IV. DISCUSSION
In order to facilitate the efficient management of fire
emergencies in an area, improvement of existing response
systems is of high significance [14]. This can be ensured by the
providing surveillance for past occurrences and understanding
of recurring patterns.
Significant Fire hotspots were manifested in both urban and
rural areas of the city and were mainly contained in Northern
portion of the study area. These were the areas having high rise
buildings, gas stations, commercial areas, suburban areas,
highways and residential areas (with heavy load shedding of
gas). As these hotspots were estimated not only for household,
commercial and secondary fires but vehicular fires as well,
various roads were also identified as hotspots. Mostly the Fire
emergencies were observed on the roads that are used by heavy
vehicles as they are more prone to overturning and catching
fire. Corcoran et al. [11] also analyzed the spatial patterns of
fire by employing GIS and obtained similar results. Rao [15]
also reported similar findings and additionally said that the
reason for Fire incidents in the city is exposed and jumbled
cable wires made of substandard material.
However, the results of the study indicated a significant
decline in the intensity of cases for the duration of the study
(Fig. 3). This declining incidence gave an account of lack of
confidence in general public to refer to firefighting
organizations and attempting to solve the matter themselves.
However the accounted figures do not represent the total
number of cases observed in the city, just the incidence that
was dealt by Rescue 1122. Other organizations at work in the
city for fire control and management include Rawalpindi Fire
Brigade Center and Qureshi Fire Control Services.
Akhter [16] explicated the reasons behind the lack of
confidence among public. The study reasoned that there is
disparity in implementation of fire safety standards in the city
as well as very little coordination among different departments
such as traffic, police and fire fighting units. This lack of
coordination along with unavailability of Incident Command
System (ICS) often translates to poor emergency response
despite good skills and training. Dawn [17] conversely reported
that local fire brigade services lack in performance due to
insufficient professional training, availability of resources,
planning and research (both pre and post fire) and nonexistence
of any fire services act for the city. All these factors, along with
lack of awareness among general public regarding fire fighting
profession has a negative impact on fire emergency response.
Hence, for improvement of future fire emergency response,
it is need of hour to understand the previous and existing
patterns, risk factors and causative agents; and ensure effective
enforcement of building and fire safety laws [18].
V. CONCLUSION AND RECOMMENDATIONS
Present study focused on providing a geostatistical
surveillance approach for ensuring future fire safety by
improving the response quality and apt resource allocation for
high risk areas. Based on the outcomes of the research, it is
concluded that there is both spatial and temporal variation in
the occurrence of Fire incidents in the study area. Most of the
Fire hotspots however were located in the Northern portion of
the study area incorporating both urban and rural areas of the
study indicating the need to shift the focus of fire service in this
region of the study area. The study further concludes that there
is a need of incorporation of GIS based surveillance system in
the rescue department to direct the response from the service
stations in a timely manner.
Therefore, the study recommends generating awareness
among people regarding fire hazard and the factors associated
with it, incorporation of GIS expertise in emergency
departments, promotion of GPS enabled cell phones in dispatch
units and fire vehicles, high level of collaboration among
different departments working in the city for fire services
provision to avoid service duplication and publication of GIS
based maps and models designed for response improvement.
ACKNOWLEDGMENT
We are endepted to the department of Rescue 1122,
Rawalpindi for providing the data regarding fire emergencies
and cooperating with us throughout this research.
REFERENCES
[1] M. I. Channa, and K. M. Ahmed, “Emergency Response
Communications and Associated Security Challenges,” Int. J.
Net. Sec. and its Appli., vol. 2 (4), pp. 179, 2012.
23

[2] W. Aiyou, S. Shiliang, L. Runqiu, T. Deming, and T. Xiafang,
“City Fire Risk Analysis based on Coupling Fault Tree Method
and Triangle Fuzzy Theory,” Proc. Engg., vol. 84, pp. 204-212,
2014.
[3] Environmental Systems Research Institute (ESRI), “Improving
Emergency Planning and Response with Geographic
Information Systems,” Redlands, New York: ESRI. Retrieved
from http://www.esri.com/library/whitepapers/pdfs/emergency-
planning-response.pdf, 2005.
[4] S. Erdogan, I. Yilmaz, T. Baybura, and M. Gullu,
“Geographical information systems aided traffic accident
system case study: city of Afyonkarahisar,” Accid. Anal. and
Prev., vol. 40, pp. 174-181, 2008.
[5] M. Kwan, and J. Lee, “Emergency response after 9/11: the
potential of real-time 3D GIS for quick emergency response in
micro-spatial environments,” Comp., Env. and Urban Sys., vol.
29, pp. 93-113, 2005.
[6] C. Yan-yan, L. Dong, and Z. Hui, “Multi-factor Risk Analysis
in a Building Fire by Two Step Cluster,” Proc. Engg., vol. 11,
pp. 658-665, 2011.
[7] M. H. Hussain, M. P. Ward, M. Body, A. Al-Rawahi, A. A.
Wadir, S. Al-Habsi, M. Saqib, M. S. Ahmed, and M. G.
Almaawali, “Spatio-temporal pattern of sylvatic rabies in the
Sultanate of Oman, 2006–2010,” Prev. Vet. Med., vol. 110, pp.
281-289, 2013.
[8] T. Ruya, M. Ning, L. Qianqian, and L. Yijun, “The Evolution
and Application of Network Analysis Methods,” IEEE Int.
Conf. on Sys., Man, and Cyber., pp.2197-2201, 2013, DOI
10.1109/SMC.2013.376.
[9] D. Dai, “Identifying clusters and risk factors of injuries in
pedestrian–vehicle crashes in a GIS environment,” J. Trans.
Geo., vol. 24, pp. 206-214, 2012.
[10] A. Spoerri, M. Egger, and E.V. Elm, “Mortality from road
traffic accidents in Switzerland: Longitudinal and spatial
analyses,” Accid. Anal. and Prev., vol. 43, pp. 40-48, 2011.
[11] J. Corcoran, G. Higgs, C. Brunsdon, A. Ware, and P. Norman,
“The use of spatial analytical techniques to explore patterns of
fire incidence: A South Wales case study,” Comp., Env. and
Urban Sys., vol. 31, pp. 623-647, 2007.
[12] B.N. Boots, and A. Getis, Point Pattern Analysis Newbury
Park. Newbury Park, CA, USA: Sage Publications, 1998.
[13] L. Fang, L. Yan, S. Liang, S. J. D. Vlas, D. Feng, X. Han, W.
Zhao, B. Xu, L. Bian, H. Yang, P. Gong, J. H. Richardus, and
W. Cao, “Spatial analysis of hemorrhagic fever with renal
syndrome in China,” BMC Infect. Dis., vol. 6, pp. 77-88, 2006.
[14] S.R. Morgan, A. M. Chang, M. Alqatari, and J. M. Pines,
“Non–Emergency Department Interventions to Reduce ED
Utilization: A Systematic Review,” Acad. Emer. Med., vol. 20,
pp. 969-985, 2013.
[15] S. Rao, “Rescue 1122 management plan finalized,” The
Nation. Retrieved from http://nation.com.pk/karachi/06-Jan-
2010/Rescue-1122-management-plan-finalised, 2010.
[16] S. Akhter, “Firefighters’ view on Improving Fire Emergency
Response: A Case Study of Rawalpindi,” Int. J. Hum. and Soc.
Sci., vol. 4(1), pp. 143-149, 2014.
[17] Dawn, “Pindi fire brigade squad runs out of steam,” Dawn.
Retrieved from http://www.dawn.com/news/90148/rawalpindi-
pindi-fire-brigade-squad-runs-out-of-steam, 2003.
[18] Pakistan Observer, “1122 Rawalpindi Rescued 1883 Emergency
Victims,” Pakistan Observer. Retrieved from
http://pakobserver.net/detailnews.asp?id=251475, 2014.
24

Prospects of Airborne Wind Energy Systems
in Pakistan
Z. H. Khan
Dept. of Electrical Engineering
Riphah International University
Islamabad, Pakistan
zeeshan.hameed@riphah.edu.pk
Sohaib Khan
Dept. of Aerospace Engineering
Institute of Space Technology
Islamabad, Pakistan
sohaibkham786@gmail.com
Arsalan Khan
Dept. of Control and Simulation
CESAT
Islamabad, Pakistan
arsalan1@mail.nwpu.edu.cn
Abstract— High altitude wind energy is considered as a high
efficiency, low cost solution to sustainable energy solutions
worldwide today due to highly flexible and adaptive designs of
powered kites. Pakistan is facing energy crisis since years due to
inefficient electrical transmission network, increased demand of
electricity, lack of resource planning and increased cost for
furnace oil which can be used to generate electricity. As a clean
energy source, conventional wind energy is a preferable choice
but it has few disadvantages due to large initial investments
involved and dangers to environment due to rotating machinery
which can result in bird hit and on the other hand, generating
acoustic noise which affect populations of people living nearby.
In response to these issues, high altitude energy is found to be
free from many such problems as found with conventional wind
energy systems. It is found suitable specially for addressing
essential energy needs of off-grid consumers for water pumping,
drilling, refrigeration of vaccines and life-saving medicines and
powering up far-off residential sites e.g. communities living off-
grid at Alaska’s northern region having minimal solar energy
during the long winter season when energy demand is greatest
for heating purposes. In this paper, we propose a UAV design
which can possibly be used as an airborne wind energy system
for electricity generation.
Keywords—Altitude wind; renewable energy; powered kites;
wind energy conversion system; distributed energy
I. INTRODUCTION
Energy production for a world’s economy is directly linked
with GDP growth. Renewable energy solutions are a preferable
choice to address the energy demands world-wide due to
environment friendly green energy technology. It has been
estimated that till 2050, 40% of the world energy requirements
will be meet by renewable energy including solar (photo
voltaic (PV) and thermal), wind, bio-mass, bio-fuel etc. [1].
Among various technologies, wind energy is preferred due to
continuous production of electricity (as long as wind is present
as prime mover) as well as high production rate with lesser
requirement of installation area as compared to solar energy
[2]. However, a detailed analysis of wind availability and on-
site surveying is mandatory for an optimal production of
energy throughout the year. The conventional wind energy
conversion systems (WECS) are popular only in those areas
where sufficient wind is available i.e. in the range of 5-8 m/s2
.
This limitation greatly affects the utilization of WECS as an
effective resource of energy as usually far off areas near sea-
shores or mountains fulfills the required energy density criteria
for feasible investments. Also, long way cabling for
transmission of this energy to grid also results in additional
cost and line losses [3]. Social activists and NGOs raise voices
and show their grievances against environmental impact of
these wind farms due to ever increasing bird accidents as of
result of collision with fast moving blade tips.
Fig 1. Airborne energy production connected to grid
This paper describes some key design features of an
airborne wind energy system (AWES) e.g. a large kite which is
flown in air while maintaining its connection with a generator
kept at ground level by a suitable rotating mechanism such as
cable drum which is linked via tether. The aerodynamic forces
acting on the kite causes traction in the cable drum which is in
turn converted into electrical power by the generator as shown
in Fig 1. The obvious advantages of shifting the heavy
mechanical components on ground result in simple design and
maximum power optimization. Due to the fact that the flying
kite operates in periodic cycles alternating between ―traction
phase‖ and ―reel-in phase‖ of the tether, the electrical power
produced is not continuous rather intermittent which is not
25

suitable for on-grid connection where continuous power is
required. However, a continuous power generation system can
be designed either by using multiple, individually controlled
kites or a battery system for buffering the power generation
across the cycles. This solution is guaranteed to generate
higher power with lower cost as compared to conventional
wind energy conversion system (WECS) due to availability of
faster wind speeds at high altitude.
Fig 2. KGS for generating electricity on-board ship
II. HIGH ALTITUDE WIND ENERGY POTENTIAL
Wind energy is a renewable resource which is not only
cheap but also readily available in most parts of the world.
Wind is discontinuous source of energy and the conventional
WECS provides uneven power supply when connected to the
grid. On the other hand, if altitude is raised, a lot more energy
is available as compared to that blowing at 50-200 m above
ground. It has been estimated that the magnitude of wind
energy above 1000 m is twice as much as found at lower
altitudes. In addition, at an altitude of 800 m above ground,
wind power density is sufficiently available to be used for
altitude energy generation all over the globe [4]. As a rule of
thumb, it has been found that a five-fold cost saving with twice
capacity increase can be achieved in shifting technology from
WECS to AWES [5].
III. ADVANTAGES OF AWES OVER WECS
A. Impact on Environment
The airborne wind energy systems have fewer effects on
environment as compared to WECS. The fast moving blades of
classical wind mills injures many birds flying in close
proximity. On the other hand, AWES has no dangerous edges
which can kill birds [6].
B. Cost comparison
Using AWES instead of WECS, an advantage of more than
90% material savings is guaranteed. This is because large
structures require more and more material to with stand heavy
loads due to wind and the rotating machinery [7]. Many tones
of weight loaded on large erected structures comprises of rotor
blades connected to a hub which drives the generator.
Moreover, the maintenance of WECS is also an issue which
requires access to the system, long down times in case of fault
and danger to the life of technician all adding up the cost
compared to AWES where generator and accessories are
installed on ground [8].
C. Mobility of the power station
In comparison with WECS, the airborne energy converter
can be moved anywhere. Example include rural areas, Coastal
belt, Desert, etc. However, if flying at higher altitudes, caution
must be paid to operate AWES in no-fly zones (NFZ) to avoid
any danger for civil aviation.
D. Efficiency
The advantage of increasing altitude as in the case of
AWES is to get more capacity due to persistent wind as
compared to turbulence due to buildings and infrastructure at
WECS heights [9].
Fig 3. Buoyant Airborne Turbine (BAT) for stand-alone energy generation in
Alaska [10]
IV. CLASSIFICATION OF AWES
The airborne wind energy systems are classified based on
their designs, grid connectivity and aerodynamics e.g. heavier
than or lighter than air configuration. Some types are indicated
as follows:
1) Flying tethered airplane and kite
In this type of AWES, a tethered kite or airplane flies in
circular or Lissajous shaped orbit to produce electricity via
traction force applied on a generator on ground or ship as
shown in Fig 2. The kite generator system (KGS) is one of the
most commonly used system due to its simplest design and
easy control system [11].
2) Multiple wing system
This type of altitude wind system has multiple wings to
generate electrical energy. A laddermill is an example of such
system [12]. Multiple wings allow scalability of the concept in
order to generate more energy.
3) Lighter than air systems
These systems are lighter than air and have an on-board
generator to produce electricity as shown in Fig 3. Such
systems are supported by a lighter than air filled balloon to
reach at 100m or above heights where enough wind is
available to drive the generator [13]. Such systems are more
suitable for areas of the world where sunlight is not available
throughout the year.
26

4) Rotor hover craft systems
These systems hover in air via rotating propellers.
Quadrotor type hovercrafts are popular which have the ease of
vertical take-off and landing operations [14]. Due to on-board
generators, the airborne system is heavier as compared to the
crosswind kite generator in which has the heavy machinery on
ground.
V. FLYING WING GENERATOR SYSTEM DESIGN
In order to visualize a practical system, we can use a flying
wing generator (FWG) for our case study. Our goal is to
evaluate optimal design parameters which can be used to
generate maximum wind at different altitudes. By using a
strong flexible tether, energy kites can reach higher altitudes of
100 to 400 meters). This can save up to 90% of the materials of
WECS used conventionally, resulting in reduced per unit
energy cost. As, these systems are more aerodynamic and can
access higher energy density due to stronger winds, each EKS
can generate 50% more energy than their conventional
counterparts [15].
A. Aerodynamics
The wind energy that can be converted to electricity is
given as:
2
3
27
2







D
L
Lw
C
C
CAvP  (1)
The relation provides some significant information about
some key design parameters. Equation 1 states that in order to
increase the power, the key parameters to increase include the
wind speed, gliding ration (L/D), the lift coefficient and the
wing surface. As a comparison, energy kites have lower L/D,
price and weight as compared to flying wing. In theory, about
60 kW can be produced per sq. meter of flying kite generator
system. The CFD model is shown in Fig 4.
(a) Side View (b) Top View (c) Front View
Fig 4. FlyPG simulation for aerodynamic analysis
In order to analyze the aerodynamic performance of our
benchmark system FlyPG, different flight conditions are used
for Aerodata generation. The aircraft wing is taken as NACA-
W-4-4410 which has a span of 4 meters, Sref equal to 1.5 m2
,
Cbar = 0.38 m, Root chord = 0.35 m, Tip chord = 0.15 m. For
the vertical tail design, NACA-V-4-0010 is selected with Root
chord = 0.25m and Tip Chord = 0.1 m. The horizontal tail
design is NACA-H-4-0010 based with root chord = 0.25 m
and Tip chord = 0.1 m.
The flight test condition is selected as Mach 0.05 at an
altitude of 200 meters above ground level. Then the angle of
attack (α) in degrees is varied from 0 to 5 degrees, while Xcg
of 0.9 meter is assumed. The aero-data is plotted in Fig 5 as a
function of angle of attack. It is important to take into account
at least 10% drag increase due to tether connecting the kite
with the ground generator.
Fig 5. Aerodata plots for FlyPG @ Mach = 0.05
B. Control and Autopilot
Modern design of FWGs incorporates automatic take
off/landing and flight for robust operation under varying
environmental conditions [16]. A coordinated control
mechanism is also devised by few manufacturers where
communication between the Kite/airplane controller and main
controller at the ground station occurs to track the given
trajectory [17].
Generally, a multi-objective loop controls the dynamics of
the system in flight. First of all, an optimal track point on
circular or Lissajous trajectory needs to be calculated and
tracked during flight [18]. In most cases a navigation loop
forms the outer one which controls the bearing of the flying
system, while an inner attitude controller controls the roll, pitch
and yaw angle as well as respective rates in order to achieve
the required bearing.
Fig 6. A generalized 2-loop control structure for Flying wing/ Energy Kite
As shown in Fig 6, an outer loop controls the bearing
(ρcom) of the flying wing. The error between the commanded
and measured bearing drives the attitude controller in order to
generate roll, pitch and yaw commands for the flying wing
until it reaches the desired navigational coordinates. In this
27

Conference proceedings

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Conference proceedings

Similar to Conference proceedings (20)

More from Dr Muhammad Mustansar

More from Dr Muhammad Mustansar (20)

Recently uploaded

Recently uploaded (20)

Conference proceedings