This document discusses control approaches for cooperative unmanned aerial vehicles (UAVs). It first presents the modeling of a quadrotor's dynamics using piecewise affine systems to capture nonlinearities. It then describes the design of an experimental quadrotor platform called UPATcopter that can estimate its state autonomously. Finally, it proposes three control strategies for quadrotors: 1) a constrained finite time optimal controller for attitude control, 2) a switching model predictive controller for trajectory/attitude control, and 3) a PID-2nd derivative controller for attitude and translation control. Experimental results demonstrate the effectiveness of these control approaches.

1. Control of Cooperative
Unmanned Aerial Vehicles
Kostas Alexis
Department of Electrical
& Computer Engineering,
University of Patras, Greece
1

2. Structure of this presentation
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 2
1. Introduction
2. Quadrotor Modeling Approach
3. Quadrotor Design
4. Quadrotor Control Approaches
5. Cooperation of UAVs
6. Conclusions and Future Work

3. History of UAVs
Some of the most wide-
spread UAV designs are
inspired from old manned
aircraft designs that did not
convince the market in their
times.
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras
1. Introduction
3
 Unmanned Aerial Vehicles can be tracked back
to the beginnings of 20th century operating in
military missions.
 UAVs started as remote piloted vehicles but
due to technological and scientific
advancements autonomous systems became
feasible.
 The end of 2oth century was a turning point in
the history of robotics: production expanded
massively to domestic use.
 Currently UAV designs for civilian applications
mainly focus in miniaturizing existing fixed-
wing and rotorcraft designs.

4. Scientific Motivation
Unmanned Aerial Vehicles and specially quadrotor rotorcrafts pose significant
scientific and engineering challenges:
 Very aggressive nonlinear, underactuated dynamics.
 They are affected from complex aerodynamic phenomena.
 Prone to perturbations due to atmospheric turbulence.
 Low-cost miniaturized sensor estimation systems are noisy, they drift and are
very prone to vibrations.
 Hard actuation constraints in terms of dynamic range, precision, response
time, nonlinear characteristics and relatively high power consumption.
 Despite their small size they are still complex machines that need increased
computational power.
Design and autonomous control of such systems is still an open challenge!
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras
1. Introduction
4

5. Socioeconomic Motivation
Unmanned Aerial Vehicles (and specially quadrotors)
can be utilized in a wide set of real-life applications:
 Intelligence, Surveillance, Reconnaissance (ISR)
 Wild-fire surveillance
 Agricultural services
 Search & Rescue
 Buildings inspection
 Area Exploration & Mapping
 Military applications
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras
1. Introduction
5

6. State of the Art
Research groups around the world
have achieved very promising results
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras
1. Introduction
6

7. Contributions of this work [1/2]
The Contributions of this work reside in the following areas:
1. Modeling of the quadrotor dynamics using Piecewise Affine systems: until
now linear and nonlinear models of the quadrotor dynamics have been proposed.
Piecewise Affine systems-based modeling provides the opportunity to:
a. capture some of the nonlinearities and couplings of the system - cover a
relatively large part of the system’s flight envelope.
b. utilize linear control theory.
2. UPATcopter Design: The UPATcopter is an efficient and modular quadrotror
experimental platform emphasizing in the areas of:
a. powerful onboard computational capabilities
b. autonomous indoor state estimation based on inertial measurements, sonar data and
vision sensors
c. extended communication options
d. low-cost but efficient actuators.
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras
1. Introduction
7

8. Contributions of this work [2/2]
3. System Control: Three different Control strategies were designed and
experimentally verified for their performance:
a. A Switching Model Predictive Controller for the 6-Degrees of Freedom trajectory control
of the quadrotor.
b. A Constrained Finite Time Optimal Controller for the quadrotor’s attitude control
problem.
c. A Proportional-Integral-Derivative-2ndDerivative /Proportional-Integral-Derivative
controller for the quadrotor’s rotational/translational motion dynamics. This control
augments classical PID controllers with angular acceleration feedback.
4. UAV cooperation: two cooperation strategies have been proposed in order to
address the problems of a) cooperative forest fire surveillance and b) area
exploration
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras
1. Introduction
8

9. Quadrotor Modeling Approach
9
1. Introduction
2. Quadrotor Modeling Approach
3. Quadrotor Design
4. Quadrotor Control Approaches
5. Cooperation of UAVs
6. Conclusions and Future Work

10. Modeling Assumptions
Quadrotor’s Forward Motion: difference in the lift
produced from the front and rear rotors
Quadrotor’s Sideward Motion: difference in the lift
produced from left and right rotors
Quadrotor’s Yaw motion: difference in the
counter-torque between the counter-rotating
rotor pairs
Perpendicular motion: rotors’ overall thrust
Dynamics modeling assumptions:
1. Rigid and symmetrical structure.
2. CoG and Body Fixed Frame coincide.
3. Rigid propellers.
4. Thrust and drag forces proportional to the
square of propeller’s speed.
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras
2. Quadrotor Modeling Approach
10

11. Forces & Moments acting on the craft [1/2]
Newton-Euler Formulation
F: force vector on the CoM
τ: total torque acting about the CoM
I3x3: indentity matrix
I: Inertia moment about the CoM
m: total mass of the body
V: acceleration of the CoM
ω: angular velocity
CoM: Center of Mass
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 11
2. Quadrotor Modeling Approach
Rotation Matrix from BFF to EFF:
Transformation of craft rates
expressed in BFF and EFF:

12. Forces & Moments acting on the craft [2/2]
Main aerodynamic forces and moments:
1. Thrust force: the resultant of the vertical forces acting on all blade elements
2. Hub force: the resultant of the horizontal forces acting on all blade elements
3. Drag moment: the moment about the rotor shaft due to aerodynamic forces
4. Rolling moment: the moment produced in forward flight when the advancing blade is
producing more lift than the retreating one
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 12
2. Quadrotor Modeling Approach
Ground Effect: when a rotorcraft is operating very close to ground (half of a
rotor’s diameter) experiences thrust augmentation due to the interference
of the surface with the airflow pattern of the rotor system.

13. Piecewise Affine Modeling Approach [1/3]
Euler-Lagrange formulation 6DOF Quadrotor Dynamics Modeling
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 13
2. Quadrotor Modeling Approach
The angles φ,θ,ψ are independent of the translational motion
Altitude motion dynamics can be decoupled from horizontal
motion dynamics

14. Piecewise Affine Modeling Approach [2/3]
Attitude Piecewise Affine representations
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 14
2. Quadrotor Modeling Approach
Discrete Time Expression
Piecewise
modeling
Augmented with Integral Terms
Disturbance effects in
attitude rates.

15. Piecewise Affine Modeling Approach [3/3]
Vertical Piecewise Affine Error Dynamics Translational Piecewise Affine Error Dynamics
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 15
2. Quadrotor Modeling Approach
Augmented with Integral Terms
Augmented with Integral Terms
Piecewise
modeling

16. Aerodynamic Effects
In classical quadrotor modeling the aerodynamic effects due to variation of the
airstream are neglected. However, even at moderate translational velocities or
for moderate wind-gusts, their impact becomes important.
1. Blade Flapping
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 16
2. Quadrotor Modeling Approach
2. Total Thrust variation
3. Airflow Disruption

17. Simulink Model
Based on experimental measurements and CAD/CAM computation a MATLAB-
Simulink model was derived in order to aid the control design process.
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 17
2. Quadrotor Modeling Approach

18. Quadrotor Modeling Conclusions
New method for the modeling of the quadrotor’s dynamics has been proposed
based on the theory of Piecewise Affine Systems.
Advantages:
 Captures nonlinearities and couplings of the system – Covers a larger part of
the quadrotor’s flight envelope compared to linear approaches.
 Takes into account the disturbance effects of atmospheric turbulence as
affine-additive terms.
 Provides the opportunity to utilize Optimal/Switching control theory.
 Can be expanded to other rotorcrafts types.
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 18
2. Quadrotor Modeling Approach

19. Quadrotor Design
19
1. Introduction
2. Quadrotor Modeling Approach
3. Quadrotor Design
4. Quadrotor Control Approaches
5. Cooperation of UAVs
6. Conclusions and Future Work

20. System Requirements
The design of the UPATcopter quadrotor experimental platform should fit the
following requirements:
 The craft should be of small-size with about 0.5m diameter.
 The craft should not exceed 1.5Kg while also providing more than 0.5Kg
additive payload.
 The craft should have high-end processing capabilities.
 The craft should be able of complete autonomous indoor and outdoor state
estimation.
 The craft should have multiple wireless communication options.
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras
3. Quadrotor Design
20

21. Experimental Platforms
In the beginning of this research a Draganflyer VTi helicopter was utilized but it
was soon proved that could not fit the aforementioned requirements.
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras
3. Quadrotor Design
21
Draganflyer Vti Toy Quadrotor
First attempt of quadrotor design

22. Experimental Platforms
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras
3. Quadrotor Design
22
Second UPATcopter platform
Final UPATcopter design
Carbon fiber centerplates
Anodized aluminum arms
Nylon/Carbon fiber propellers
s

23. UPATcopter main hardware diagram
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 23
3. Quadrotor Design

24. Main Control Unit
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 24
3. Quadrotor Design
All – in – one Single Board Computers can be utilized as Powerful, low-power, low-weight and low-
cost Main Control Units.
This approach provides the capability to rapidly develop and deploy control, cooperation and
environmental perception algorithms using high-level programming methods and high-end
operation systems.
picoITX was selected as the Main Control Unit of UPATcopters • 1.6GHz ATOM Z530 Processor
able to cope with all required
control and perception
computations
• 2GB RAM
• Modular connectivity through
USB Ports, I2C Bus, SPI – All
sensors can be easily used
• Easily combined with Wireless
Networks adapters
• Less than 0.5A at 5V
• Less than 250g with Memory and
SSD Hard Disk Drive

25. Sensor System – Attitude/Altitude Estimation
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 25
3. Quadrotor Design
Xsens MTi-G Attitude
Heading Reference System
Open-Source IMUs
12oHz Maximum update rate
Relatively low drifting
Closed firmware
100Hz Maximum update rate
Drifting
Very prone to vibrations
30o degrees beam sonar
provides altitude data

26. Sensor System – Indoor horizontal motion estimation
One of the most demanding problems of complete indoor state estimation is
that of horizontal motion measurement and estimation. This problem can be
solved either by using fixed cameras (higher accuracy, very high cost, not
autonomous solution) or by designing onboard position estimation systems.
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 26
3. Quadrotor Design
2 Optic Flow Systems were Developed
Mouse Sensor Based Optic Flow Tam 2Micro Vision chip solution
The Tam2 vision chip based optic flow solution implements the Image Interpolation
Algorithm (I2A) in order to derive optic flow measurements from the pixel array.
I2A algorithm computes the amplitude of the translation sd between an image
region I(n,t) captured at time t, and a later image I(n,t+Δt)

27. Propulsion Group
 Accurate control of motor-propeller control is critical in order to achieve increased
flight accuracy.
 DC – brushless motors were utilized due to their increased torque characteristics.
 The appropriate programming of the Electronic Speed Controller in high update rates
(>100Hz/I2C Bus), the power consumption and the identification of the final Speed
Controller-Motor-Propeller system is important for the overall control problem.
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 27
3. Quadrotor Design

28. Quadrotor Control Approaches
28
1. Introduction
2. Quadrotor Modeling Approach
3. Quadrotor Design
4. Quadrotor Control Approaches
5. Cooperation of UAVs
6. Conclusions and Future Work

29. Proposed Control Strategies
 A Constrained Finite Time Optimal Control (CFTOC) Strategy for the Quadrotor’s
attitude set-point problem.
 A Switching Model Predictive Control (SMPC) Strategy for the Quadrotor’s
trajectory/attitude control problem.
 A Proportional-Integral-Derivative-2nd Derivative/ Proportional-Integral-Derivative
Control Strategy for the Quadrotor’s Attitude/Translational dynamics.
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 29
4. Quadrotor Control Approaches

30. Constrained Finite Time Attitude Optimal Control
System Piecewise Affine Dynamics:
Goals:
Capture nonlinearities of the attitude subsystem
Account for state and input constraints of the system
Account for the additive effects of wind-gust disturbances
Explicit Solution – Offline Computation
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4. Quadrotor Control Approaches

31. Constrained Finite Time Attitude Optimal Control
Input Constraints:
State Constraints
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4. Quadrotor Control Approaches

32. Constrained Finite Time Attitude Optimal Control
Assuming Ts sampling period
Compute the optimal control sequence:
Cost function subject to PWA dynamics:
The control action is a continuous function of the following form:
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 32
4. Quadrotor Control Approaches
Convex Polyhedron
Number of created polyhedra

33. Constrained Finite Time Attitude Optimal Control
Experimental studies with an initial experimental set-up consisted of a Draganflyer VTi
quadrotor, Xsens MTi-G IMU and personal computer.
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 33
4. Quadrotor Control Approaches
Tait-Bryan angle rates are
not equal with p,q,r rates
s

34. Constrained Finite Time Attitude Optimal Control
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 34
4. Quadrotor Control Approaches

35. Constrained Finite Time Attitude Optimal Control
Attitude Regulation for 1-3-5 PWA systems
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 35
4. Quadrotor Control Approaches

36. Constrained Finite Time Attitude Optimal Control
Attitude Regulation for 1-3-5 PWA systems subject to forcible Wind-Gusts
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 36
4. Quadrotor Control Approaches

37. Constrained Finite Time Attitude Optimal Control
Comparison of LQ (red) – CFTOC (blue)
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 37
4. Quadrotor Control Approaches

38. Constrained Finite Time Attitude Optimal Control
Response subject to different directional wind-gusts
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 38
4. Quadrotor Control Approaches

39. Constrained Finite Time Attitude Optimal Control
In conclusion:
1. CFTOC can be computed over a family of Piecewise Affine systems.
2. Ensures stability among the switching.
3. Accounts for the state and input constraints of the system.
4. Efficient in wind-gust disturbances attenuation.
5. Multi-Parametric solution has the advantage of off-line computation.
6. However: excessive computational cost for systems with more than 4 states
and prediction horizon larger than 5 steps ahead– inefficient onboard
implementation. This is due to the exponential number of transitions
between regions which can occur when a controller is computed in a
dynamic programming fashion.
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 39
4. Quadrotor Control Approaches

40. Switching Model Predictive Control
Why Model Predictive Control:
1. It handles multivariable control problems naturally.
2. It can take into account actuator and state limitations.
3. It allows operation close to constraints – more profitable operation.
4. Receding horizon ‘idea’ can lead to smoother response
Why not:
1. Increased computational costs.
2. Requires good knowledge of the model.
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 40
4. Quadrotor Control Approaches
Kontron pITX &
good programming ==
Problem Solved!
Can be solved with
extended simulations,
CAD tools and
experimental studies.
Main contributions of the proposed SMPC strategy:
1. First time to design and experimentally verify a Model Predictive Control for
the quadrotor’s attitude and trajectory control problem.
2. Switching control based on multiple Piecewise Affine system
representations.
3. Accounts for the additive effects of wind-gusts due to affine terms in the
model.
4. Accounts for the state and input constraints of the quadrotor.
5. Experimentally verified based on Inertial sensors, Sonar and Optic Flow
position deviation measurements. The proposed system achieves accurate
position control both in the absence and under the presence of wind-gusts,
trajectory tracking and accurate attitude maneuvering.
Submitted at IET Control Theory and
Applications

41. Switching Model Predictive Control
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 41
4. Quadrotor Control Approaches

42. Switching Model Predictive Control
Discretized Attitude, Altitude and Horizontal Piecewise Affine Dynamics:
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 42
4. Quadrotor Control Approaches

43. Switching Model Predictive Control
State and Input Constraints:
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 43
4. Quadrotor Control Approaches
Attitude Constraints Vertical Constraints Horizontal Constraints

44. Switching Model Predictive Control
For each Piecewise Affine system:
with respect to the control moves and the Piecewise Affine
system dynamics, where:
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 44
4. Quadrotor Control Approaches
Prediction horizon (=5)
Control horizon (=2)

45. Switching Model Predictive Control
Selected Piecewise Operation Regions and Linearization Points (Γ>>1):
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 45
4. Quadrotor Control Approaches

46. Switching Model Predictive Control
Position Hold:
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 46
4. Quadrotor Control Approaches

47. Switching Model Predictive Control
Position Hold under Wind-Gusts:
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 47
4. Quadrotor Control Approaches

48. Switching Model Predictive Control
Trajectory Control:
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 48
4. Quadrotor Control Approaches

49. Switching Model Predictive Control
Hover subject in the absence and under the presence of wind-gusts
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 49
4. Quadrotor Control Approaches

50. Switching Model Predictive Control
Attitude Maneuver
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 50
4. Quadrotor Control Approaches

51. Switching Model Predictive Control
In conclusion:
1. Model Predictive Control is very promising for such complex multivariable
systems.
2. It poses significant advantages including its abilities to account for the
physical constraints of the system and the effects of atmospheric
disturbances.
3. Switching control counts for some of the nonlinearities of the system and
produces control actions for a large part of the quadrotor’s flight envelope.
4. The drawback of increased computational costs can be resolved.
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 51
4. Quadrotor Control Approaches

52. PIDD/PID Attitude/Position Control
Proportional – Integral – Derivative – 2nd Derivative Attitude Control
1. Based on classical PID theory augmented with angular acceleration
feedback.
2. Extends the bandwidth of the closed-loop system – provides the opportunity
to increase the control gains.
3. Leads to faster tracking response.
Translational dynamics are relatively slow – classical PID was utilized
This control law was designed in order to use the computational power of the
Kontron pITX for other computations (Environmental Perception)
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 52
4. Quadrotor Control Approaches

53. PIDD/PID Attitude/Position Control
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 53
4. Quadrotor Control Approaches

54. PIDD/PID Attitude/Position Control
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 54
4. Quadrotor Control Approaches

55. PIDD/PID Attitude/Position Control
Position Hold:
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 55
4. Quadrotor Control Approaches

56. PIDD/PID Attitude/Position Control
Attitude Regulation
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 56
4. Quadrotor Control Approaches

57. PIDD/PID Attitude/Position Control
Aggressive Attitude Regulation
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 57
4. Quadrotor Control Approaches

58. PIDD/PID Attitude/Position Control
PIDD/PID control:
1. Combination of classical PID control schemes and angular acceleration
feedback.
2. Efficient & Simple – low computational cost.
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 58
4. Quadrotor Control Approaches

59. Cooperation of Unmanned
Aerial Vehicles
59
1. Introduction
2. Quadrotor Modeling Approach
3. Quadrotor Design
4. Quadrotor Control Approaches
5. Cooperation of UAVs
6. Conclusions and Future Work

60. Cooperating UAVs
UAVs are utilized in more complex missions and specially ISR (Intelligence, Surveillance
and Reconnaisance). Such missions pose a number of challenging requirements:
1. Environmental Perception
2. Navigation under Uncertainty
3. Mission Critical Issues (i.e. timing)
4. Redundancy
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 60
5. Cooperation of UAVs
Cooperative UAVs

61. Cooperative Forest-Fire Surveillance
 This mission is solved using an overlapping cooperation approach.
 The overlapping strategy is based on simple Consensus implemented in each
UAV, while the communication between the UAVs is carried according to the
consensus algorithm:
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 61
5. Cooperation of UAVs

62. Cooperative Forest-Fire Surveillance
Forest Fire propagation model required for simulation studies:
 Although there are complex models based on statistic data, simple geometrical
models that assume that the fire perimeter expands like an ellipse or a folium in
relation with the wind’s blowing direction are adequate for UAV cooperation
simulation studies.
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 62
5. Cooperation of UAVs

63. Cooperative Forest-Fire Surveillance
Operation goal: track every point of the evolving fire perimeter and update the location
of the fire with the least latency.
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 63
5. Cooperation of UAVs
Objective: Equalize the paths flown by the quadrotors

64. Cooperative Forest-Fire Surveillance
Decentralized Solution Algorithm
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 64
5. Cooperation of UAVs

65. Simulation Test Case:
1. The fire perimeter is an ellipse and the evolution is modeled as a change on the ellipse
foci affecting its major and minor radius. The change in perimeter occurs in T=1hr,2hrs
and 4hs respectively.
2. Each quadrotor communicates only with the quadrotor with which they meet in a
rendezvous point.
3. Quadrotors that have met in a rendezvous point sense fire perimeter changes. If a
variation of the perimeter is sensed then both quadrotors fly to the new fire
perimeter at its closed point.
Cooperative Forest-Fire Surveillance
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5. Cooperation of UAVs

66. Heterogeneous UAV Swarm Area Exploration and Target
Acquisition
Heterogeneous UAV Swarm – Cooperation strategies should account for the different
vehicle capabilities, advantages and drawbacks.
Assume a UAV Swarm consisted of Quadrotors with different characteristics
Quadrotors can fly forward but also hover above a target for constant surveillance: the
cooperation strategy must also benefit from this capability
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 66
5. Cooperation of UAVs
The goal of the UAV-Swarm is to enter an
unknown fixed dimensions area with
static targets, explore as soon as
possible, acquire the target positions and
constantly survey them.

67. Heterogeneous UAV Swarm Area Exploration and Target
Acquisition
Cooperation Strategy assumptions:
1. The fixed dimensions unexplored area is tessellated into equal sized square Cells, Cell(k,m).
2. Each UAV is equipped with two vision systems for forward look and downwards area
exploration.
3. Vi is the velocity factor of the i-th UAV
4. Ci is the camera factor expressing the exploration camera
capabilities of the i-th UAV
1. Ei is the remaining flight endurance of the i-th UAV
2. Ti is the number indicating the total allocated tasks from the i-th UAV
3. Li is the number indicating the accomplish tasks from the i-th UAV
4. Ai is the uncompleted allocated tasks to the i-th UAV
5. Ri is the number of the maximum tasks to be executed in one round
6. Mi is a binary value indicating if the i-th UAV is in hovering mode
7. Xi is a binary value indicating if the i-th UAV is available for exploration (it is not when it has
acquired a target)
8. fi is the front camera omnidirectional range of the i-th UAV
9. cvi are the cells viewed from the exploration camera of the i-th UAV at each time
10. dik,m is the distance between the i-th UAV and the center of Cell(k,m)
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 67
5. Cooperation of UAVs

68. Heterogeneous UAV Swarm Area Exploration and Target
Acquisition
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 68
5. Cooperation of UAVs

69. Heterogeneous UAV Swarm Area Exploration and Target
Acquisition
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 69
5. Cooperation of UAVs

70. Conclusions & Future Work
70
1. Introduction
2. Quadrotor Modeling Approach
3. Quadrotor Design
4. Quadrotor Control Approaches
5. Cooperation of UAVs
6. Conclusions and Future Work

71. Conclusions & Future Work
The main contributions of this Thesis are:
 New Quadrotor dynamics modeling based on Piecewise Affine Systems theory
 Design of a powerful and modular quadrotor experimental platform capable of very complex
computations, complete autonomous indoor state estimation and extended communication
capabilities.
 Design and Experimental verification of Constrained Finite Time Optimal Controllers, Switching
Model Predictive Control strategies and PIDD/PID control schemes.
 Two new UAV cooperation strategies were proposed in order to address the problems of forest
fire surveillance and area exploration.
Future Work:
1. Design of Miniature/Micro Aerial Vehicles based on novel flying concepts: a) convertible fixed-
wing to rotorcraft or inspired by nature, and b) transformerable UGV-UAV, AUV-UAV
2. Design of control laws and environmental perception that would ensure precise navigation
under severe environmental disturbances by studying the nature of the aerodynamic effects
3. Design and experimental verification of fully decentralized cooperation strategies for large
heterogeneous UAV swarms
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 71
6. Conclusions & Future Work

72. Conclusions & Future Work
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 72
6. Conclusions & Future Work
UAV WSN
Micro-Quadrotor
Environmental
Perception
Convertible UAV: Tilt-Rotor
KA-GN
CP-KA
Samara Blade

73. Thank you for your
attention!
Thank you for your
attention!
Control of Cooperative Unmanned Aerial Vehicles – Kostas Alexis, University of Patras 73
Control of Cooperative Unmanned Aerial Vehicles