This document describes a simulation of a quadcopter's dynamic system and interactive control system created by researchers at George Mason University. The simulation models the quadcopter's behavior in different environments based on parameters like payload, desired altitude, ascent/descent speeds and hover time. It includes equations of motion for the quadcopter in the z-axis and models forces like thrust, drag, weight. The simulation was able to follow user-specified altitude profiles within 1m of accuracy even with payloads over half the quadcopter's weight, demonstrating the potential for quadcopters as safe and effective delivery systems.
Quadcopters are the rotorcraft which have become the catch of the eye in the UAVs, both for electronic hobbyists as well as various application based real time solutions.
Abstract This paper presents the design and implementation of a quadcopter capable of payload delivery. A quadcopter is a unique unmanned aerial vehicle which has the capability of vertical take-off and landing. In this design, the quadcopter was controlled wirelessly from a ground control station using radio frequency. It was modeled mathematically considering its attitude and altitude, and a simulation carried out in MATLAB by designing a proportional Integral Derivative (PID) controller was applied to a mathematical model. The PID controller parameters were then applied to the real system. Finally, the output of the simulation and the prototype were compared both in the presence and absence of disturbances. The results showed that the quadcopter was stable and able to compensate for the external disturbances.
Abstract This paper presents the design and implementation of a quadcopter capable of payload delivery. A quadcopter is a unique unmanned aerial vehicle which has the capability of vertical take-off and landing. In this design, the quadcopter was controlled wirelessly from a ground control station using radio frequency. It was modeled mathematically considering its attitude and altitude, and a simulation carried out in MATLAB by designing a proportional Integral Derivative (PID) controller was applied to a mathematical model. The PID controller parameters were then applied to the real system. Finally, the output of the simulation and the prototype were compared both in the presence and absence of disturbances. The results showed that the quadcopter was stable and able to compensate for the external disturbances.
PID vs LQR controller for tilt rotor airplane IJECEIAES
The main thematic of this paper is controlling the main manoeuvers of a tilt rotor UAV airplane in several modes such as vertical takeoff and landing, longitudinal translation and the most important phase which deal with the transition from the helicopter mode to the airplane mode and visversa based on a new actuators combination technique for specially the yaw motion with not referring to rotor speed control strategy which is used in controlling the attitude of a huge number of vehicles nowadays. This new actuator combination is inspired from that the transient response of a trirotor using tilting motion dynamics provides a faster response than using rotor speed dynamics. In the literature, a lot of control technics are used for stabilizing and guarantee the necessary manoeuvers for executing such task, a multiple Attitude and Altitude PID controllers were chosen for a simple linear model of our tilt rotor airplane in order to fulfill the desired trajectory, for reasons of complexity of our model the multiple PID controller doesnt take into consideration all the coupling that exists between the degrees of freedom in our model, so an LQR controller is adopted for more feasible solution of complex manoeuvering, the both controllers need linearization of the model for an easy implementation.
Quadcopters are the rotorcraft which have become the catch of the eye in the UAVs, both for electronic hobbyists as well as various application based real time solutions.
Abstract This paper presents the design and implementation of a quadcopter capable of payload delivery. A quadcopter is a unique unmanned aerial vehicle which has the capability of vertical take-off and landing. In this design, the quadcopter was controlled wirelessly from a ground control station using radio frequency. It was modeled mathematically considering its attitude and altitude, and a simulation carried out in MATLAB by designing a proportional Integral Derivative (PID) controller was applied to a mathematical model. The PID controller parameters were then applied to the real system. Finally, the output of the simulation and the prototype were compared both in the presence and absence of disturbances. The results showed that the quadcopter was stable and able to compensate for the external disturbances.
Abstract This paper presents the design and implementation of a quadcopter capable of payload delivery. A quadcopter is a unique unmanned aerial vehicle which has the capability of vertical take-off and landing. In this design, the quadcopter was controlled wirelessly from a ground control station using radio frequency. It was modeled mathematically considering its attitude and altitude, and a simulation carried out in MATLAB by designing a proportional Integral Derivative (PID) controller was applied to a mathematical model. The PID controller parameters were then applied to the real system. Finally, the output of the simulation and the prototype were compared both in the presence and absence of disturbances. The results showed that the quadcopter was stable and able to compensate for the external disturbances.
PID vs LQR controller for tilt rotor airplane IJECEIAES
The main thematic of this paper is controlling the main manoeuvers of a tilt rotor UAV airplane in several modes such as vertical takeoff and landing, longitudinal translation and the most important phase which deal with the transition from the helicopter mode to the airplane mode and visversa based on a new actuators combination technique for specially the yaw motion with not referring to rotor speed control strategy which is used in controlling the attitude of a huge number of vehicles nowadays. This new actuator combination is inspired from that the transient response of a trirotor using tilting motion dynamics provides a faster response than using rotor speed dynamics. In the literature, a lot of control technics are used for stabilizing and guarantee the necessary manoeuvers for executing such task, a multiple Attitude and Altitude PID controllers were chosen for a simple linear model of our tilt rotor airplane in order to fulfill the desired trajectory, for reasons of complexity of our model the multiple PID controller doesnt take into consideration all the coupling that exists between the degrees of freedom in our model, so an LQR controller is adopted for more feasible solution of complex manoeuvering, the both controllers need linearization of the model for an easy implementation.
Modeling and control approach to a distinctive quadrotor helicopterISA Interchange
The referenced quadrotor helicopter in this paper has a unique configuration. It is more complex than commonly used quadrotors because of its inaccurate parameters, unideal symmetrical structure and unknown nonlinear dynamics. A novel method was presented to handle its modeling and control problems in this paper, which adopts a MIMO RBF neural nets-based state-dependent ARX (RBF-ARX) model to represent its nonlinear dynamics, and then a MIMO RBF-ARX model-based global LQR controller is proposed to stabilize the quadrotor's attitude. By comparing with a physical model-based LQR controller and an ARX model-set-based gain scheduling LQR controller, superiority of the MIMO RBF-ARX model-based control approach was confirmed. This successful application verified the validity of the MIMO RBF-ARX modeling method to the quadrotor helicopter with complex nonlinearity.
Modeling and Roll, Pitch and Yaw Simulation of Quadrotor.Oka Danil
In this paper, we developed a prototype of a quadrotor and proposes how to model and conduct simulations to investigate the effect of roll, pitch and yaw as the inputs to the outputs of φ, θ and ψ angle in quadrotor. The Euler-Newton formalism is used to model the dynamic system. The simulation results show that, the majority of φ angle is
determined by the roll, most of the θ angle is determined by the pitch, and the ψ angle is determined by the yaw.
Design and Implementation of a Quadrotor HelicopterHicham Berkouk
This is a project on building a quadrotor from scratch. From the history; physics and modeling to system hardware and software. The control algorithm is built arround a PID loop. For more details, please feel free to comment or send me an email to: hicham.berkouk@outlook.com
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
CONCEPT OF OPERATIONS TO SYSTEM DESIGN AND DEVELOPMENT-AN INTEGRATED SYSTEM F...ijics
In recent times, there has been a significant rise in usage of aircrafts in surveillance and reconnaissance missions. Not all the aircrafts survive the harsh testing conditions put forth by the enemy regions. Aircraft Survivability Analysis gives the measure of the chances of survival for different counter strategies. The mission would be recalculated if particular sortie does not fall within the physical boundary of the
performance of an aircraft. This is required both for the success of the mission and the survivability of the
aircraft in the harsh enemy conditions.
CONCEPT OF OPERATIONS TO SYSTEM DESIGN AND DEVELOPMENT-AN INTEGRATED SYSTEM F...ijcisjournal
In recent times, there has been a significant rise in usage of aircrafts in surveillance and reconnaissance missions. Not all the aircrafts survive the harsh testing conditions put forth by the enemy regions. Aircraft Survivability Analysis gives the measure of the chances of survival for different counter strategies. The mission would be recalculated if particular sortie does not fall within the physical boundary of the performance of an aircraft. This is required both for the success of the mission and the survivability of the aircraft in the harsh enemy conditions.
A system is envisioned comprising of the accurate modeling of the physical world and the accurate model of control system. An interoperable system which can work seamlessly together will provide mission planners, System integrators, aeronautical/aerospace engineers a milieu wherein the Control System designer who is found wanted as far as the physical world is concerned is given a system which can simulate the real world in lab conditions. To achieve this, we combine the two most promising environments prevalent in the industry today namely Systems tool kit for modeling the operational environment MATLAB and LabVIEW for modeling the control system environment. Using a Math script window of LabVIEW, we have designed the aircraft model and controlling the variables of an aircraft using a simulation loop of a LabVIEW. The different flight conditions were arrived using Orthogonal Array (OA) based on different Aircraft weight, Altitude, Mach number configurations. This attempts to span the aircrafts across the regimes in aircrafts flight envelope. A system comprising of both, with seamless UDP based connection between the two is developed to expedite the process of development of feasible control system design and verification which allows the aircrafts to undertake complex mission. This system we believe would answer questions of limits of the aircrafts maneuverability and survivability in terms of its limitation concerning control system design and development of commercial fighter aircrafts, UAV's and Quad copters.
The research of 6-DOF flight simulator washout filter Control MethodIJRES Journal
Electric 6-DOF flight simulator used in large aircraft engineering simulation has great benefits,As a Flight Simulator vector parallel six degree of freedom motion system is a very important part of flight simulator. Feeling is the most important in Flight simulator test while flight.If a flight simulator can feel closer to the real feeling of flying aircraft, in is more better for trainning.According to the question above, In this paper, we will start from the control method,make research on electric 6-DOF flight simulator wash out the filter control method, we will research Longitudinal studies of flight parameters at takeoff position flight simulator. Using MATLAB simulation software to verify washout filter algorithm practicality simulator Simulation.
Optimal and pid controller for controlling camera’s position in unmanned aeri...Zac Darcy
This paper describes two controllers designed specifically for adjusting camera’s position in a small unmanned aerial vehicle (UAV). The optimal controller was designed and first simulated by using MATLAB technique and the results displayed graphically, also PID controller was designedand simulatedby using MATLAB technique .The goal of this paper is to connect the tow controllers in cascade mode to obtain the desired performance and correction in camera’s position in both roll and pitch.
Optimal and Pid Controller for Controlling Camera's Position InUnmanned Aeria...Zac Darcy
This paper describes two controllers designed specifically for adjusting camera’s position in a small
unmanned aerial vehicle (UAV). The optimal controller was designed and first simulated by using
MATLAB technique and the results displayed graphically, also PID controller was designedand
simulatedby using MATLAB technique .The goal of this paper is to connect the tow controllers in cascade
mode to obtain the desired performance and correction in camera’s position in both roll and pitch.
Adaptive control of nonlinear system based on QFT application to 3-DOF flight...TELKOMNIKA JOURNAL
Research on unmanned aerial vehicle (UAV) became popular because of remote flight access and
cost-effective solution. 3-degree of freedom (3-DOF) unmanned helicopters is one of the popular research
UAV, because of its high load carrying capacity with a smaller number of motor and requirement of
forethought motor control dynamics. Various control algorithms are investigated and designed for the motion
control of the 3DOF helicopter. Three-degree-of-freedom helicopter model configuration presents the same
advantages of 3-DOF helicopters along with increased payload capacity, increase stability in hover,
manoeuvrability and reduced mechanical complexity. Numerous research institutes have chosen
the three-degree-of-freedom as an ideal platform to develop intelligent controllers. In this research paper,
we discussed about a hybrid controller that combined with Adaptive and Quantitative Feedback theory (QFT)
controller for the 3-DOF helicopter model. Though research on Adaptive and QFT controller are not a new
subject, the first successful single Adaptive aircraft flight control systems have been designed for the U.S.
Air Force in Wright Laboratories unmanned research vehicle, Lambda [1]. Previously researcher focused on
structured uncertainties associated with controller for the flight conditions theoretically. The development of
simulationbased design on flight control system response, opened a new dimension for researcher to design
physical flight controller for plant parameter uncertainties. At the beginning, our research was to investigates
the possibility of developing the QFT combined with Adaptive controller to control a single pitch angle that
meets flying quality conditions of automatic flight control. Finally, we successfully designed the hybrid
controller that is QFT based adaptive controller for all the three angles.
Development and Implementation of a Washout Algorithm for a 6-dof Motion Plat...IJRES Journal
Flight simulators for pilot training is extremely important due safety and economic factors.
Flight simulator needs to simulate different kinds of complicated motion state such as roll, pitch and yaw
angles. It has six-degree of freedom, high precision, high rigid, modular design and many other advantages. The
motion system responds to the aircraft linear and angular accelerations in order to compute the most
appropriate cabin motion to replicate these accelerations, subject to the displacement limits and the velocity
limits of the actuators. The cabin accelerations are filtered in order to compute the most appropriate cabin
motion to replicate the actual airplane accelerations. This paper developed and implemented a motion washout
algorithm that can enhance the fidelity of motion platform and the cabin motion never exceeds the mechanical
limits of the motion platform, particularly the maximum actuator displacements and the maximum actuator
velocities.
Improvement of Pitch Motion Control of an Aircraft SystemsTELKOMNIKA JOURNAL
The movement of the aircraft pitch is very important to ensure the passengers and crews are in
intrinsically safe and the aircraft achieves its maximum stability.The objective of this study is to provide a
solution to the control system that features particularly on the pitch angle motion of aircraft systemin order
to have a comfort boarding. Three controllers were developed in these projects which wereproportional
integral derivative (PID), fuzzy logic controller (FLC), and linear quadratic regulator (LQR) controllers.
These controllers will help improving the pitch angle and achievingthe target reference. By improving the
pitch motion angle, the flight will be stabilized and in steady cruise (no jerking effect), hence provides all
the passengers withthe comfort zone. Simulation results have been done and analyzed using Matlab
software. The simulation results demonstrated LQR and FLC were better than PID in the pitch motion
system due to the small error performance. In addition, withstrong external disturbances, a single controller
is unable to control the system, thus, the combination of PID and LQR managed to stabilize the aircraft.
Automation of Air Traffic Management Using Fuzzy Logic Algorithm to Integrate...IJECEIAES
Unmanned Aircraft Systems (UAS) have been increasing in popularity in personal, commercial, and military applications. The increase of the use of UAS poses a significant risk to general air travel, and will burden an already overburdened Air Traffic Control (ATC) network if the Air Traffic Management (ATM) system does not undergo a revolutionary change. Already there have been many near misses reported in the news with personal hobbyist UAS flying in controlled airspace near airports almost colliding with manned aircraft. The expected increase in the use of UAS over the upcoming years will exacerbate this problem, leading to a catastrophic incident involving substantial damage to property or loss of life. ATC professionals are already overwhelmed with the air traffic that exists today with only manned aircraft. With UAS expected to perform many tasks in the near future, the number of UAS will greatly outnumber the manned aircraft and overwhelm the ATC network in short order to the point where the current system will be rendered extremely dangerous, if not useless. This paper seeks to explore the possibility of using the artificial intelligence concept of fuzzy logic to automate the ATC system in order to handle the increased traffic due to UAS safely and efficiently. Automation would involve an algorithm to perform arbitration between aircraft based on signal input to ATC ground stations from aircraft, as well as signal output from the ATC ground stations to the aircraft. Fuzzy logic would be used to assign weights to the many different variables involved in ATM to find the best solution, which keeps aircraft on schedule while avoiding other aircraft, whether they are manned or unmanned. The fuzzy logic approach would find the weighted values for the available variables by running a simulation of air traffic patterns assigning different weights per simulation run, over many different runs of the simulation, until the best values are found that keep aircraft on schedule and maintain the required separation of aircraft.
Stability Control of an Autonomous Quadcopter through PID Control LawIJERA Editor
In the recent years the world has seen a astonishing ascendance of non tripulated vehicles, and among these is the quadrotors aircrafts or quadcopters. These types of aircraft have been of particular interest due to its easy maneuverability in closed and open spaces and somewhat simplified dynamics. In these paper is presented an first attempt in the built model, to control the 4 DOF(Degrees of freedom) of an soon to be autonomous quadcopter through PID law in an controlled environment.
1. George Mason University
Quadcopter Dyamics
Dynamic Systems Model and Interactive Control System
George Mason University
Systems Engineering and Operational Research
Fairfax, United States of America
Adnan Khan
Pwint Htwe
Luis Soto
Waqar Chaudhry
Badar Alwetaid
Unmanned aerial vehicles (UAV) have transformed our world
by enabling us to remotely perform actions never before possible.
From military purposes to filmmaking, UAV’s are now even
available to everyday citizens with little oversight. Due to the
increasing use of these machines in industry and now the civilian
sphere, it is in our interest to model their behavior in different
environments and explore their capabilities.
I. INTRODUCTION
The control systems of quadcopters are a major subject of
research as these vehicles find their place in the aerial world.
Their interaction with other aerial and ground-based units
requires us to refine and develop their controls so they maintain
safety standards and better serve our needs.
This report covers an exercise in which a quadcopter was
modeled to follow a user-specified altitude profile which
includes parameters such as payload, desired altitude, desired
ascent and descent speeds, and hover time.
II. MODEL DESCRIPTION AND EQUATIONS
A. Assumptions
The model was simplified in order to place greater
emphasis on user-interaction and system response than
physical quadcopter design parameters. Relationships between
rotor speed, battery life, and lift were not taken into account.
All four rotors are seen to be providing one upward thrust force
which is assumed to be 31.4 Newtons [1]. Note that maximum
thrust is not used in the “Max Thrust” gain as it was not
required to meet system requirements. The unit weight,
dimensions, and temperature range were modeled after the DJI
Phantom 3 professional [2].
Drag force is modeled using an air density value based on a
median temperature of 68°F, a mean national elevation of
2500ft and 50% humidity. Noise blocks were added to both the
position and velocity to simulate atmospheric changes, wind
turbulence and other minor weather behavior. The body was
modeled as a cube and a coefficient value of 0.8 was used [3].
A delay is added after the controller via the “Action Delay”
block which consists of a transfer function. This provides a
one-second delay which models the delay in system response
after the command executes. An altitude sensor is added to
provide a feedback loop allowing the system to conform to the
altitude profile.
Figure 1: Free body diagram (Ascent)
B. Model Description
The free body diagram for the system can be seen in figure
1 above. Individual thrust forces are shown in the diagram on
each rotor (Tx forces were summed into a single upward
thrust) and the downward thrust and weight forces are
represented by a single vector. Only movement in the z-axis is
modeled and the three phases are ascent, descent and hover. In
the hover phase, the drag force is absent and in the descent
phase it faces in the opposite direction (upward). The figure
below shows both of these phases:
Figure 2: Hover (left) and descent (right)
C. Equations
The equation of motion shown on the next page gives the
acceleration of the quadcopter in the z-axis as 𝑧̈. The parameter
T represents thrust which is the control. m represents the mass
of the quadcopter, while mp represents the mass of the payload.
This mass is entered in the MATLAB script file provided with
the simulation as a weight in pounds and exported to the
Simulink diagram. The drag equation complicates the model
2. George Mason University
due to the 𝑧̇2
term which is not a common Laplace transform.
Laplace-domain analyses such as the final value theorem and
root-locus plots are no longer possible. The reliance then shifts
toward the numerical method provided by Simulink.
𝑧̈ =
𝑇
𝑚 + 𝑚 𝑝
−
9.8(𝑚 + 𝑚 𝑝) + 0.11𝑧̇2
𝑚 + 𝑚 𝑝
Once the altitude profile is added and the feedback loop is run
from the output, the thrust parameter T becomes a gain and is
used to control the system. The implementation can be seen in
figure 3 of the Appendix where the T parameter is named
“Max thrust”. Note the saturation block added after the altitude
profile. This block serves no other purpose than to filter out all
negative inputs from the altitude profile. The “sign” block
before the squared velocity term allows the system to retain the
negative sign of the velocity despite the term being squared.
III. RESULTS
A. Tables and Figures
The Appendix contains the Simulink diagram for the model
(Figure 3) as well as various plots (Figures 4-6). The noise
function makes the graphs difficult to interpret but this is a
realistic constraint because at only 2.8lbs, the vehicle is fairly
sensitive to wind and other external forces. The tables
accompanying the plots display the inputs entered by the user.
A wide range of values were chosen for each response to
model system behavior at different states. The response
plotted in figure 6 has particularly large values which are
extreme cases designed to test the system at its limits. The DJI
Phantom 3 Professional has maximum ascent and descent
speeds of 5m/s and 3m/s, respectively.
B. Analysis
One system requirements dictates that it must not deviate
from the user-specified altitude profile by more than 1m. The
standard error plot shows the actual altitude subtracted by the
input profile altitude. The scope is connected before the thrust
gain which gives an accurate representation of deviation. In
“System Response 3”, the effect of the large payload of 1.5lbs
(more than half the weight of the vehicle) is evident by the
larger standard error. However, the system still meets the
project specifications with considerable room for error. Due to
the large thrust potential of the rotors, deviation from the
specified profile is quick and enough to keep the quadcopter on
course with great accuracy. It is also less sensitive to higher
payloads and has a high payload capacity. It should be noted
that the error may be caused by large velocity values as well,
since the other two system responses have lower magnitudes of
standard error.
The system meets the landing velocity requirement as well
in every case despite visible oscillation from the noise blocks.
System velocity appears to be much smoother in response 1
which is likely due to the smaller ascent and descent speeds.
Velocity oscillations are also caused by the abrupt changes in
the altitude profile which can be seen in the landing phases of
each system response. Same-size matrices were used to
produce the altitude profile which may not be the best way of
modeling a flight plan. A decaying exponential or another
continuously time-based function may allow for a smoother
landing, especially when the ascent and descent speeds are of
larger magnitude. At t = 10 in the first response plot, the
oscillations caused by the abrupt change in command can be
seen in the velocity plot. The same destabilizing behavior can
be seen in the same plot at approximately t =28, and t = 35. I is
worthy of noting that the abrupt rise in error is more prevalent
at other parts of the graph than the landing phases. System
performance could be utilized further if a smoother altitude
profile was made. A smoother input would cause fewer
oscillations, increase vehicle payload and maximize ascent and
descent speeds.
CONCLUSION
The model shows the potential of the four-rotor quadcopter
to be safe, efficient and extremely useful. The maximum thrust
value used in this model shows that the vehicle can pick up
more than half its weight as a payload and still meet the
required deviation limits. As a general rule, greater payload or
mass seems to require a proportional increase in the maximum
allowable thrust that the rotors produce. The simulation
conducted demonstrates the viability of the quadcopter as a
safe and effective low-cost delivery system. Moreover, the
model can be modified for use with different physical
parameters with ease. In conclusion, this model can be used to
explore the limits of quadcopters as their immense application
potential is explored.
References
[1] E310 - Specs | DJI. (n.d.). Retrieved December 11, 2015, from
http://www.dji.com/product/e310/spec?www=v1
[2] Phantom 3 Professional - Specs, FAQ, Tutorials, Downloads
and DJI GO | DJI. (n.d.). Retrieved December 11, 2015, from
http://www.dji.com/product/phantom-3-pro/info#spec
[3] Drag Coefficient. (n.d.). Retrieved December 11, 2015, from
http://www.engineeringtoolbox.com/drag-coefficient-d_627.html
3. George Mason University
IV: Appendix
Figure 3 (Simulink diagram):
Figure 4 (System Response 1):
0 5 10 15 20 25 30 35 40 45 50
-5
0
5
10
15
20
25
Time (seconds)
Altitude(meters)
Quadcopter System Response
System Resonse
Altitude Profile
56.5 57 57.5 58 58.5 59 59.5 60
-0.3
-0.2
-0.1
0
0.1
0.2
Time (seconds)
Velocity(m/s)
Quadcopter Velocity
Velocity
0 10 20 30 40 50 60
-0.2
0
0.2
0.4
0.6
Time(seconds)
Error(meters)
QuadcopterSystem Error
System Error
User-Specified Response Parameters
Ascent speed 2.0 m/s
Descent speed 3.0 m/s
Payload weight 0.7 lbs
Altitude 20.0 m
Hover time 17.0 s
4. George Mason University
Figure 5 (System Response 2):
0 50 100 150 200 250 300
-0.4
-0.2
0
0.2
0.4
Time (seconds)
error(meters)
Quadcopter Altitude Error
error
0 50 100 150 200 250 300
-50
0
50
100
150
200
250
300
350
Time (seconds)
Altitude(meters)
Quadcopter System Response
System Resonse
Altitude Profile
218 220 222 224 226 228 230 232
-0.2
-0.1
0
0.1
0.2
Time (seconds)
Velocity(m/s)
Quadcopter Velocity
Velocity
User-Specified Response Parameters
Ascent speed 4.0 m/s
Descent speed 5.0 m/s
Payload weight 0.2 lbs.
Altitude 300.0 m
Hover time 25.0 s
5. George Mason University
Figure 6 (System Response 3):
0 50 100 150 200 250 300
-1
-0.5
0
0.5
1
Time (seconds)
Systemerror(meters)
Quadcopter System Error
System Error
0 20 40 60 80 100 120 140 160
-50
0
50
100
150
200
250
300
Time (seconds)
Altitude(meters)
Quadcopter System Response
System Resonse
Altitude Profile
292 293 294 295 296 297 298 299 300
-0.2
-0.1
0
0.1
0.2
Time (seconds)
Velocity(m/s)
Quadcopter System Velocity
User-Specified Response Parameters
Ascent speed 7.0 m/s
Descent speed 9.0 m/s
Payload weight 1.5 lbs.
Altitude 250 m
Hover time 50 s
6. George Mason University
V: TEAM MEMBERS
Adnan Khan is a Systems Engineering undergraduate student at George Mason University and a former certified Mercedes-Benz
repair technician. He is specializing in the aviation concentration and is working toward a career in the automotive or aerospace
industry.
Pwint Htwe is a junior in System Engineering with concentration on aviation. She was born and raised in Myanmar and came to
US to further her education.
Luis Soto is a junior in Systems Engineering with a concentration in engineering systems. He was born in Peru and lived there for
twelve years before immigrating to the United States. He hopes to apply the knowledge he gains at George Mason University to
further his career.
Waqar Chaudhry is a senior in Systems Engineering with a concentration in financial engineering. He was born in Pakistan and
immigrated to the United States to pursue higher education. He intends to start his own business as well as get involved in the
medical field of developing countries.
Bader Alwetaid is a senior in Systems Engineering with a concentration in financial engineering. He is an active member of IEEE
(Institute of Electrical and Electronics Engineers) as well as other student organizations.