This presentation delves into the intricate realm of drone flight control, employing the robust framework of PID (Proportional-Integral-Derivative) controllers. As drones surge in prominence across various industries, mastering their flight dynamics becomes paramount. This presentation, tailored for senior students at ASTU's Electrical Power and Control Engineering Department, embarks on a journey to demystify the fundamentals of drone flight control and elucidate the design and simulation intricacies of PID controllers. Through this presentation, attendees will garner insights into the indispensable components underpinning drone flight control systems. From the pivotal role of sensors in capturing crucial flight data to the pivotal task of actuators in translating control signals into physical motion, every facet of the flight control ecosystem will be dissected. The presentation will also delve into the theoretical foundations of PID controllers, unraveling the nuanced interplay between Proportional, Integral, and Derivative components. Understanding these elements lays the groundwork for designing effective control systems capable of maintaining drone stability, ensuring precise navigation, and facilitating smooth maneuvering. Armed with this knowledge, attendees will be guided through the step-by-step process of designing PID controllers specifically tailored for drone flight control. The presentation will explore methodologies for system identification and delve into controller tuning techniques, including the renowned Ziegler-Nichols method, to optimize controller performance. Furthermore, the presentation will culminate in a simulation showcase, where attendees will witness the PID controller in action through immersive simulations. These simulations will provide a tangible demonstration of the controller's efficacy in stabilizing drone flight, navigating through varying environmental conditions, and executing complex maneuvers with precision. Ultimately, this presentation serves as a comprehensive guide for aspiring engineers, equipping them with the requisite knowledge and skills to navigate the dynamic landscape of drone flight control with confidence and proficiency

1. Presented by Selam B.
DESIGN AND SIMULATION
OF DRONE FLIGHT CONTROL
USING PID CONTROLLER
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2. Our Great Team
Selam Bizualem
Tsion Tsegaye
Belay Demeke
Abdulkerim Seid
Wendimagegne Ashenafi
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3. Am going to present about
Introduction
01
Problem
02
Objective
03
Limitation of The Study
04
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4. INTRODUCTION
• Quadcopter is a type of unmanned aerial
vehicle (UAV) that is powered by four rotors
typically use brushless motors for their rotors
• The trend in unmanned aerial vehicle (UAV)
usage is not for a limited purpose but also for
multi-purpose by modifying some parts
• Quadcopter has 6 degrees of freedom (three
translational and three rotational) and only four
actuators
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5. Three Rotational Movements
1. Roll
2. Pitch
3. Yaw
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6. PROBLEMS
why we are going to control its flight?
01 02 04
03
• Instability • Oscillations • Drifting • Inability to respond to
disturbances
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7. Objective
EFFECTIVE AND
EFFICIENT CONTROL
SYSTEM DESIGN FOR
IMPROVED FLIGHT
PERFORMANCE.
OBTAIN THE
MATHEMATICAL MODEL
OF QUADCOPTER
DYNAMICS
DESIGN LINEAR PID
CONTROLLERS FOR THE
QUADCOPTER
IMPLEMENTING THE
CONTROLLERS ON
MATLAB SIMULINK
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8. Limitation of The Study
 SIMPLIFICATION OF REAL-WORLD DYNAMICS
 INADEQUACY IN HANDLING NON-LINEAR BEHAVIORS
 MODELING ERRORS
 DATA QUALITY AND AVAILABILITY
 ENVIRONMENTAL FACTORS
 SCALABILITY ISSUES
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9. Mathematical Modeling
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Presented by Belay D.
▪ Simplified Assumption
▪ Reference Frame
▪ Rotational Matrix
▪ Newton Euler Method
▪ State Space Representation
▪ Linearization

10. 01. REFERENCE FRAMET
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11. 03 ROTATIONAL MATRIX
The Euler rotation about Rxyz
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12. • The Newton-Euler method can be used to develop a set of
differential equations that describe the quadcopter's translational
and rotational motion.
Newton Euler Method
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13. Translational and Rotational Motion
of Equation for Quadcopter
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14. Page - 14
Linearization

15. Page - 15
State Space representation

16. Presented by Wendimagegne A.
PID CONTROLLER
DESIGN
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17.  What Is PID Controller?
 How does it work?
 What is the output of PID Controller?
 What is the main objective of PID Controller?
PID CONTROLLER DESIGN

18. PID CONTROLLER DESIGN
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19. PID Controller Design for Altitude, Heading
and Attitude Control of Quadcopter
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20. There are six control inputs that also must be formulated.
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21. Designing The Altitude Controller for The Simplified Model
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22. Presented by Abdulkreim S.
Simulation Results and
Analysis of Drone Flight
Control Using PID
Controllers
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23. Position Controller
Tracking Performance
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24. Heading (Yaw) Controller Tracking
Performance 𝝍
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25. Roll Controller (𝝓)
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Pitch Controller (𝜽)

26. PID Control Signals
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27. Reference Trajectory
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28. Helix Trajectory
Reference Trajectory
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29. Simulation Diagram of
Quadcopter Page - 29

30. v
Am going to present about
over view of past related worrk
compare our work with previous
did we solve our problem
01
02
03
04 conclusion
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Presented by Tsion T.

31. Over View Of Past Related
Work
• Etienne Oehmichen - was the first scientist to experiment
with a rotating wing in 1920
• George de Bothezat and Ivan - developed their own X-
shaped aircraft which consisted of four six-wing blades
• H. Bolandi - Optimized PID Designed for attitude control
only and effect of matchings
• Thusoo, R - PID Altitude and attitude control only
controller not designed for X and Y axis
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32. Previous Works Our Project
• Was controlled manually by the pilot
• PID Designed for attitude control only
• Unmanned aerial vehicle UAV
• PID designed for all Parameters
• Had limited flight time and range
• Can fly for longer duration and cover a great
distance
• We saw different kind of trajectory path like
helix and manual reference
Compare Our Work With Previous
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33. Did We Solve Our Problem
•Instability
By using PID controller proportional, integral,
derivative.
•Oscillations
Tune the system: Carefully tuning the system parameters
through experimentation and analysis.
•Drifting
By using PID controller proportional, integral,
derivative.
•Inability to respond
to disturbance
Tune the system: Carefully tuning the system parameters
through experimentation and analysis.
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34. Conclusion
• The performance of proposed controller compared to PID
controller with different time domain metrics like rise
time, settling time and overshoot. Moreover, error
between all.
• Cascaded inner-outer loop trajectory tracking designed
for quadrotor trajectory tracking..
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35. Conclusion
• The performance PID shows good work in both time
domain specification and absolute error histogram in
terms of rise, settling time and overshoot..
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36. Thank You!
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Any Questions?