Unmanned Aerial Vehicles
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Unmanned Aerial Vehicles : Embedded Control

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Description

This book presents the basic tools required to obtain the dynamicalmodels for aerial vehicles (in the Newtonian or Lagrangianapproach). Several control laws are presented for mini-helicopters,quadrotors, mini-blimps, flapping-wing aerial vehicles, planes,etc. Finally, this book has two chapters devoted to embeddedcontrol systems and Kalman filters applied for aerial vehiclescontrol and navigation. This book presents the state of the art inthe area of UAVs. The aerodynamical models of differentconfigurations are presented in detail as well as the controlstrategies which are validated in experimental platforms.
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Product details

  • Hardback | 344 pages
  • 157 x 239 x 24mm | 658g
  • London, United Kingdom
  • English
  • 1. Auflage
  • 1848211279
  • 9781848211278
  • 1,863,344

Table of contents

Chapter 1. Aerodynamic Configurations and Dynamic Models1 Pedro CASTILLO and Alejandro DZUL 1.1. Aerodynamic configurations 1 1.2. Dynamic models 6 1.2.1. Newton-Euler approach 7 1.2.2. Euler-Lagrange approach 9 1.2.3. Quaternion approach 10 1.2.4. Example: dynamic model of a quad-rotor rotorcraft 13 1.3. Bibliography 20 Chapter 2. Nested Saturation Control for Stabilizing thePVTOL Aircraft 21 Isabelle FANTONI and Amparo PALOMINO 2.1. Introduction 21 2.2. Bibliographical study 22 2.3. The PVTOL aircraft model 24 2.4. Control strategy 25 2.4.1. Control of the vertical displacement y 26 2.4.2. Control of the roll angle and the horizontaldisplacement x 27 2.5. Other control strategies for the stabilization of the PVTOLaircraft 33 2.6. Experimental results 33 2.7. Conclusions 38 2.8. Bibliography 38 Chapter 3. Two-Rotor VTOLMini UAV: Design, Modeling andControl 41 Juan ESCARENO, Sergio SALAZAR and Eduardo RONDON 3.1. Introduction 41 3.2. Dynamic model 43 3.2.1. Kinematics 44 3.2.2. Dynamics 44 3.2.3. Model for control analysis 48 3.3. Control strategy 48 3.3.1. Altitude control 49 3.3.2. Horizontal motion control 49 3.3.3. Attitude control 50 3.4. Experimental setup 51 3.4.1. Onboard flight system (OFS) 52 3.4.2. Outboard visual system 53 3.4.3. Experimental results 55 3.5. Concluding remarks 56 3.6. Bibliography 56 Chapter 4. Autonomous Hovering of a Two-Rotor UAV59 Anand SANCHEZ, Juan ESCARENO and Octavio GARCIA 4.1. Introduction 59 4.2. Two-rotor UAV 60 4.2.1. Description 61 4.2.2. Dynamic model 61 4.3. Control algorithm design 67 4.4. Experimental platform 73 4.4.1. Real-time PC-control system (PCCS) 73 4.4.2. Experimental results 74 4.5. Conclusion 76 4.6. Bibliography 77 Chapter 5. Modeling and Control of a Convertible Plane UAV79 Octavio GARCIA, Juan ESCARENO and Victor ROSAS 5.1. Introduction 79 5.2. Convertible plane UAV80 5.2.1. Vertical mode 80 5.2.2. Transition maneuver 81 5.2.3. Horizontal mode 81 5.3. Mathematical model 81 5.3.1. Translation of the vehicle 82 5.3.2. Orientation of the vehicle 83 5.3.3. Equations of motion 85 5.4. Controller design 86 5.4.1. Hover control 86 5.4.2. Transition maneuver control 96 5.4.3. Horizontal flight control 102 5.5. Embedded system 106 5.5.1. Experimental platform 106 5.5.2. Microcontroller 108 5.5.3. Inertial measurement unit (IMU) 109 5.5.4. Sensor fusion 109 5.6. Conclusions and future works 111 5.6.1. Conclusions 111 5.6.2. Future works 112 5.7. Bibliography 112 Chapter 6. Control of Different UAVs with Tilting Rotors115 Juan ESCARENO, Anand SANCHEZ and Octavio GARCIA 6.1. Introduction 115 6.2. Dynamic model of a flying VTOL vehicle 116 6.2.1. Kinematics 117 6.2.2. Dynamics 118 6.3. Attitude control of a flying VTOL vehicle 119 6.4. Triple tilting rotor rotorcraft: Delta 119 6.4.1. Kinetics of Delta 120 6.4.2. Torques acting on the Delta 121 6.4.3. Experimental setup 123 6.4.4. Experimental results 125 6.5. Single tilting rotor rotorcraft: T-Plane 127 6.5.1. Forces and torques acting on the vehicle 127 6.5.2. Experimental results 129 6.6. Concluding remarks 131 6.7. Bibliography 132 Chapter 7. Improving Attitude Stabilization of a Quad-RotorUsingMotor Current Feedback 133 Anand SANCHEZ, Luis GARCIA-CARRILLO, Eduardo RONDON and OctavioGARCIA 7.1. Introduction 133 7.2. Brushless DC motor and speed controller 134 7.3. Quad-rotor 138 7.3.1. Dynamic model 139 7.4. Control strategy 140 7.4.1. Attitude control 140 7.4.2. Armature current control 142 7.5. System configuration 144 7.5.1. Aerial vehicle 145 7.5.2. Ground station 146 7.5.3. Vision system 147 7.6. Experimental results 148 7.7. Concluding remarks 150 7.8. Bibliography 151 Chapter 8. Robust Control Design Techniques AppliedtoMini-Rotorcraft UAV: Simulation and Experimental Results153 Jose Alfredo GUERRERO, Gerardo ROMERO, Rogelio LOZANO andEfrain ALCORTA 8.1. Introduction 153 8.2. Dynamic model 155 8.3. Problem statement 156 8.4. Robust control design 158 8.5. Simulation and experimental results 160 8.5.1. Simulations 160 8.5.2. Experimental platform 162 8.6. Conclusions 164 8.7. Bibliography 164 Chapter 9. Hover Stabilization of a Quad-Rotor Using a SingleCamera 167 Hugo ROMERO and Sergio SALAZAR 9.1. Introduction 167 9.2. Visual servoing 168 9.2.1. Direct visual servoing 169 9.2.2. Indirect visual servoing 169 9.2.3. Position based visual servoing 170 9.2.4. Image-based visual servoing 171 9.2.5.Position-image visual servoing 172 9.3. Camera calibration 173 9.3.1. Two-plane calibration approach 173 9.3.2. Homogenous transformation approach 175 9.4. Pose estimation 177 9.4.1. Perspective of n-points approach 177 9.4.2. Plane-pose-based approach 179 9.5. Dynamic model and control strategy 181 9.6. Platform architecture 183 9.7. Experimental results 184 9.7.1. Camera calibration results 185 9.7.2. Testing phase 185 9.7.3. Real-time results 185 9.8. Discussion and conclusions 186 9.9. Bibliography 188 Chapter 10. Vision-Based Position Control of a Two-Rotor VTOLMini UAV 191 Eduardo RONDON, Sergio SALAZAR, Juan ESCARENO and RogelioLOZANO 10.1. Introduction 191 10.2. Position and velocity estimation 193 10.2.1. Inertial sensors 193 10.2.2. Visual sensors 193 10.2.3. Kalman-based sensor fusion 198 10.3. Dynamic model 200 10.4. Control strategy 203 10.4.1. Frontal subsystem (Scamy) 203 10.4.2. Lateral subsystem (Scamx) 204 10.4.3. Heading subsystem (S ) 204 10.5. Experimental test bed and results 204 10.5.1. Experimental results 206 10.6. Concluding remarks 207 10.7. Bibliography 207 Chapter 11. Optic Flow-Based Vision System for Autonomous 3DLocalization and Control of Small Aerial Vehicles 209 Farid KENDOUL, Isabelle FANTONI and Kenzo NONAMI 11.1. Introduction 209 11.2. Related work and the proposed 3NKF framework 210 11.2.1. Optic flow computation 210 11.2.2.Structure from motion problem 212 11.2.3. Bioinspired vision-based aerial navigation 213 11.2.4. Brief description of the proposed framework 213 11.3. Prediction-based algorithm with adaptive patch foraccurate and efficient opticflowcalculation 215 11.3.1. Search center prediction 215 11.3.2. Combined block-matching and differential algorithm216 11.4. Optic flow interpretation for UAV 3D motion estimation andobstacles detection (SFMproblem) 219 11.4.1. Imaging model 219 11.4.2. Fusion of OF and angular rate data 220 11.4.3. EKF-based algorithm for motion and structure estimation221 11.5. Aerial platform description and real-time implementation223 11.5.1. Quadrotor-based aerial platform 223 11.5.2. Real-time software 225 11.6. 3D flight tests and experimental results 227 11.6.1. Experimental methodology and safety procedures 227 11.6.2. Optic flow-based velocity control 227 11.6.3. Optic flow-based position control 229 11.6.4. Fully autonomous indoor flight using optic flow 231 11.7. Conclusion and future work 233 11.8. Bibliography 234 Chapter 12. Real-Time Stabilization of an Eight-Rotor UAVUsing Stereo Vision and Optical Flow 237 Hugo ROMERO, Sergio SALAZAR and Jose GOMEZ 12.1. Stereo vision 238 12.2. 3D construction 242 12.3. Keypoints matching algorithm 245 12.4. Optical flow-based control 245 12.4.1. Lucas-Kanade approach 247 12.5. Eight-rotorUAV 249 12.5.1. Dynamic model 249 12.5.2. Control strategy 257 12.6. System concept 259 12.7. Real-time experiments 260 12.8. Bibliography 263 Chapter 13. Three-Dimensional Localization 265 Juan Gerardo CASTREJON-LOZANO and Alejandro DZUL 13.1. Kalman filters 266 13.1.1. Linear Kalman filter 266 13.1.2. Extended Kalman filter 269 13.1.3. Unscented Kalman filter 270 13.1.4. Spherical simplex sigma-point Kalman filters 278 13.2. Robot localization 285 13.2.1. Types of localization 285 13.2.2. Inertial navigation theoretical framework 286 13.3. Simulations 289 13.3.1.Quad-rotorhelicopter 289 13.3.2. Inertial navigation simulations 290 13.3.3. Conclusions 296 13.4. Bibliography 297 Chapter 14. Updated Flight Plan for an Autonomous Aircraft ina Windy Environment 301 Yasmina BESTAOUI and Fouzia LAKHLEF 14.1. Introduction 301 14.2. Modeling 304 14.2.1. Down-draftmodeling 304 14.2.2. Translational dynamics 305 14.3. Updated flight planning308 14.3.1. Basic problem statement 310 14.3.2. Hierarchical planning structure 311 14.4. Updates of the reference trajectories: time optimalproblem 312 14.5. Analysis of the first set of solutionsS1 315 14.6. Conclusions 323 14.7. Bibliography 323 List of Authors 327 Index 331
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About Rogelio Lozano

Rogelio Lozano, University of Technology of Compiegne, France.
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