Unmanned Aerial Vehicles

Name: Unmanned Aerial Vehicles | Embedded Control
Brand: Wiley-ISTE
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Embedded Control

Rogelio Lozano(Herausgeber*in)

Wiley-ISTE (Verlag)

Erschienen am 4. Februar 2013

352 Seiten

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978-1-118-59997-6 (ISBN)

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Inhalt

Cover
Unmanned Aerial Vehicles
Title Page
Copyright Page
Table of Contents
Chapter 1. Aerodynamic Configurations and Dynamic Models
1.1. Aerodynamic configurations
1.2. Dynamic models
1.2.1. Newton-Euler approach
1.2.2. Euler-Lagrange approach
1.2.3. Quaternion approach
1.2.4. Example: dynamic model of a quad-rotor rotorcraft
1.3. Bibliography
Chapter 2. Nested Saturation Control for Stabilizing the PVTOL Aircraft
2.1. Introduction
2.2. Bibliographical study
2.3. The PVTOL aircraft model
2.4. Control strategy
2.4.1. Control of the vertical displacement y
2.4.2. Control of the roll angle ? and the horizontal displacement x
2.4.2.1. Boundedness of ?
2.4.2.2. Boundedness of ?
2.4.2.3. Boundedness of x
2.4.2.4. Boundedness of x
2.4.2.5. Convergence of ?, ?, x and x to zero
2.5. Other control strategies for the stabilization of the PVTOL aircraft
2.6. Experimental results
2.7. Conclusions
2.8. Bibliography
Chapter 3. Two-Rotor VTOL Mini UAV: Design, Modeling and Control
3.1. Introduction
3.2. Dynamic model
3.2.1. Kinematics
3.2.2. Dynamics
3.2.2.1. Forces acting onthe vehicle
3.2.2.2. Torques acting on the vehicle
3.2.3. Model for control analysis
3.3. Control strategy
3.3.1. Altitude control
3.3.2. Horizontal motion control
3.3.3. Attitude control
3.4. Experimental setup
3.4.1. Onboard flight system (OFS)
3.4.2. Outboard visual system
3.4.2.1. Position
3.4.2.2. Optical flow
3.4.3. Experimental results
3.5. Concluding remarks
3.6. Bibliography
Chapter 4. Autonomous Hovering of a Two-Rotor UAV
4.1. Introduction
4.2. Two-rotor UAV
4.2.1. Description
4.2.2. Dynamic model
4.2.2.1. Translational motion
4.2.2.2. Rotational motion
4.2.2.3. Reduced model
4.3. Control algorithm design
4.4. Experimental platform
4.4.1. Real-time PC-control system (PCCS)
4.4.1.1. Sensors and communication hardware
4.4.2. Experimental results
4.5. Conclusion
4.6. Bibliography
Chapter 5. Modeling and Control of a Convertible Plane UAV
5.1. Introduction
5.2. Convertible plane UAV
5.2.1. Vertical mode
5.2.2. Transition maneuver
5.2.3. Horizontal mode
5.3. Mathematical model
5.3.1. Translation of the vehicle
5.3.2. Orientation of the vehicle
5.3.2.1. Euler angles
5.3.2.2. Aerodynamic axes
5.3.2.3. Torques
5.3.3. Equations of motion
5.4. Controller design
5.4.1. Hover control
5.4.1.1. Axial system
5.4.1.2. Longitudinal system
5.4.1.3. Lateral system
5.4.1.4. Simulation and experimental results
5.4.2. Transition maneuver control
5.4.3. Horizontal flight control
5.5. Embedded system
5.5.1. Experimental platform
5.5.2. Microcontroller
5.5.3. Inertial measurement unit (IMU)
5.5.4. Sensor fusion
5.6. Conclusions and future works
5.6.1. Conclusions
5.6.2. Future works
5.7. Bibliography
Chapter 6. Control of Different UAVs with Tilting Rotors
6.1. Introduction
6.2. Dynamic model of a flying VTOL vehicle
6.2.1. Kinematics
6.2.2. Dynamics
6.3. Attitude control of a flying VTOL vehicle
6.4. Triple tilting rotor rotorcraft: Delta
6.4.1. Kinetics of Delta
6.4.2. Torques acting on the Delta
6.4.3. Experimental setup
6.4.3.1. Avionics
6.4.3.2. Sensor module (SM)
6.4.3.3. On-board microcontroller (OBM)
6.4.3.4. Data acquisition module (DAQ)
6.4.4. Experimental results
6.5. Single tilting rotor rotorcraft: T-Plane
6.5.1. Forces and torques acting on the vehicle
6.5.2. Experimental results
6.5.2.1. Experimental platform
6.5.2.2. Experimental test
6.6. Concluding remarks
6.7. Bibliography
Chapter 7. Improving Attitude Stabilization of a Quad-Rotor Using Motor Current Feedback
7.1. Introduction
7.2. Brushless DC motor and speed controller
7.3. Quad-rotor
7.3.1. Dynamic model
7.4. Control strategy
7.4.1. Attitude control
7.4.2. Armature current control
7.5. System configuration
7.5.1. Aerial vehicle
7.5.2. Ground station
7.5.3. Vision system
7.6. Experimental results
7.7. Concluding remarks
7.8. Bibliography
Chapter 8. Robust Control Design Techniques Applied to Mini-Rotorcraft UAV: Simulation and Experimental Results
8.1. Introduction
8.2. Dynamic model
8.3. Problem statement
8.4. Robust control design
8.5. Simulation and experimental results
8.5.1. Simulations
8.5.2. Experimental platform
8.6. Conclusions
8.7. Bibliography
Chapter 9. Hover Stabilization of a Quad-Rotor Using a Single Camera
9.1. Introduction
9.2. Visual servoing
9.2.1. Direct visual servoing
9.2.2. Indirect visual servoing
9.2.3. Position based visual servoing
9.2.4. Image-based visual servoing
9.2.5. Position-image visual servoing
9.3. Camera calibration
9.3.1. Two-plane calibration approach
9.3.2. Homogenous transformation approach
9.4. Pose estimation
9.4.1. Perspective of n-points approach
9.4.2. Plane-pose-based approach
9.5. Dynamic model andcontrol strategy
9.6. Platform architecture
9.7. Experimental results
9.7.1. Camera calibration results
9.7.2. Testing phase
9.7.3. Real-time results
9.8. Discussion and conclusions
9.9. Bibliography
Chapter 10. Vision-Based Position Control of a Two-Rotor VTOL Mini UAV
10.1. Introduction
10.2. Position and velocity estimation
10.2.1. Inertial sensors
10.2.2. Visual sensors
10.2.2.1. Position
10.2.2.2. Optical flow (OF)
10.2.3. Kalman-based sensor fusion
10.3. Dynamic model
10.4. Control strategy
10.4.1. Frontal subsystem (Scamy)
10.4.2. Lateral subsystem (Scamx)
10.4.3. Heading subsystem (S?)
10.5. Experimental testbed and results
10.5.1. Experimental results
10.6. Concluding remarks
10.7. Bibliography
Chapter 11. Optic Flow-Based Vision System for Autonomous 3D Localization and Control of Small Aerial Vehicles
11.1. Introduction
11.2. Related work and the proposed 3NKF framework
11.2.1. Optic flow computation
11.2.2. Structure from motion problem
11.2.3. Bioinspired vision-based aerial navigation
11.2.4. Brief description of the proposed framework
11.3. Prediction-based algorithm with adaptive patch for accurate and efficient optic flow calculation
11.3.1. Search center prediction
11.3.2. Combined block-matching and differential algorithm
11.3.2.1. Nominal OF computation using a block-matching algorithm (BMA)
11.3.2.2. Subpixel OF computation using a differential algorithm (DA)
11.4. Optic flow interpretation for UAV 3D motion estimation and obstacles detection (SFM problem)
11.4.1. Imaging model
11.4.2. Fusion of OF and angular rate data
11.4.3. EKF-based algorithm for motion and structure estimation
11.5. Aerial platform description and real-time implementation
11.5.1. Quadrotor-based aerial platform
11.5.2. Real-time software
11.6. 3D flight tests and experimental results
11.6.1. Experimental methodology and safety procedures
11.6.2. Optic flow-based velocity control
11.6.3. Optic flow-based position control
11.6.4. Fully autonomous indoor flight using optic flow
11.7. Conclusion and future work
11.8. Bibliography
Chapter 12. Real-Time Stabilization of an Eight-Rotor UAV Using Stereo Vision and Optical Flow
12.1. Stereo vision
12.2. 3D reconstruction
12.3. Keypoints matching algorithm
12.4. Optical flow-based control
12.4.1. Lucas-Kanade approach
12.5. Eight-rotor UAV
12.5.1. Dynamic model
12.5.1.1. Translational subsystem model
12.5.1.2. Rotational subsystem model
12.5.2. Control strategy
12.5.2.1. Attitude control
12.5.2.2. Horizontal displacements and altitude control
12.6. System concept
12.7. Real-time experiments
12.8. Bibliography
Chapter 13. Three-Dimensional Localization
13.1. Kalman filters
13.1.1. Linear Kalman filter
13.1.2. Extended Kalman filter
13.1.3. Unscented Kalman filter
13.1.3.1. UKF algorithm
13.1.3.2. Additive UKF algorithm
13.1.3.3. Square-root UKF algorithm
13.1.3.4. Additive square-root UKF algorithm
13.1.4. Spherical simplex sigma-point Kalman filters
13.1.4.1. Spherical simplex sigma-point approach
13.1.4.2. Spherical simplex UKF algorithm
13.1.4.3. Additive SS-UKF Algorithm
13.1.4.4. Square-root SS-UKF algorithm
13.1.4.5. Square-root additive SS-UKF algorithm
13.2. Robot localization
13.2.1. Types of localization
13.2.1.1. Dead reckoning (navigation systems)
13.2.1.2. A priori map-based localization
13.2.1.3. Simultaneous localization and mapping (SLAM)
13.2.2. Inertial navigation theoretical framework
13.2.2.1. Navigation equations in the navigation frame
13.3. Simulations
13.3.1. Quad-rotor helicopter
13.3.2. Inertial navigation simulations
13.3.3. Conclusions
13.4. Bibliography
Chapter 14. Updated Flight Plan for an Autonomous Aircraft in a Windy Environment
14.1. Introduction
14.2. Modeling
14.2.1. Down-draft modeling
14.2.2. Translational dynamics
14.3. Updated flight planning
14.3.1. Basic problem statement
14.3.2. Hierarchical planning structure
14.4. Updates of the reference trajectories: time optimal problem
14.5. Analysis of the first set of solutions S1
14.6. Conclusions
14.7. Bibliography
List of Authors
Index

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