Unmanned Aerial Vehicles

Embedded Control

Inbunden, Engelska, 2010

Av Rogelio Lozano, France) Lozano, Rogelio (UTC

2 609 kr

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This book presents the basic tools required to obtain the dynamical models for aerial vehicles (in the Newtonian or Lagrangian approach). 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 embedded control systems and Kalman filters applied for aerial vehicles control and navigation. This book presents the state of the art in the area of UAVs. The aerodynamical models of different configurations are presented in detail as well as the control strategies which are validated in experimental platforms.

Produktinformation

Utgivningsdatum2010-05-11
Mått163 x 241 x 24 mm
Vikt649 g
FormatInbunden
SpråkEngelska
Antal sidor352
FörlagISTE Ltd and John Wiley & Sons Inc
ISBN9781848211278

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Elektronik och kommunikationer inom Naturvetenskap och teknik

Chapter 1. Aerodynamic Configurations and Dynamic Models 1Pedro CASTILLO and Alejandro DZUL1.1. Aerodynamic configurations 11.2. Dynamic models 61.2.1. Newton-Euler approach 71.2.2. Euler-Lagrange approach 91.2.3. Quaternion approach 101.2.4. Example: dynamic model of a quad-rotor rotorcraft 131.3. Bibliography 20Chapter 2. Nested Saturation Control for Stabilizing the PVTOL Aircraft 21Isabelle FANTONI and Amparo PALOMINO2.1. Introduction 212.2. Bibliographical study 222.3. The PVTOL aircraft model 242.4. Control strategy 252.4.1. Control of the vertical displacement y 262.4.2. Control of the roll angle θ and the horizontal displacement x 272.5. Other control strategies for the stabilization of the PVTOL aircraft 332.6. Experimental results 332.7. Conclusions 382.8. Bibliography 38Chapter 3. Two-Rotor VTOLMini UAV: Design, Modeling and Control 41Juan ESCARENO, Sergio SALAZAR and Eduardo RONDON3.1. Introduction 413.2. Dynamic model 433.2.1. Kinematics 443.2.2. Dynamics 443.2.3. Model for control analysis 483.3. Control strategy 483.3.1. Altitude control 493.3.2. Horizontal motion control 493.3.3. Attitude control 503.4. Experimental setup 513.4.1. Onboard flight system (OFS) 523.4.2. Outboard visual system 533.4.3. Experimental results 553.5. Concluding remarks 563.6. Bibliography 56Chapter 4. Autonomous Hovering of a Two-Rotor UAV 59Anand SANCHEZ, Juan ESCARENO and Octavio GARCIA4.1. Introduction 594.2. Two-rotor UAV 604.2.1. Description 614.2.2. Dynamic model 614.3. Control algorithm design 674.4. Experimental platform 734.4.1. Real-time PC-control system (PCCS) 734.4.2. Experimental results 744.5. Conclusion 764.6. Bibliography 77Chapter 5. Modeling and Control of a Convertible Plane UAV 79Octavio GARCIA, Juan ESCARENO and Victor ROSAS5.1. Introduction 795.2. Convertible plane UAV805.2.1. Vertical mode 805.2.2. Transition maneuver 815.2.3. Horizontal mode 815.3. Mathematical model 815.3.1. Translation of the vehicle 825.3.2. Orientation of the vehicle 835.3.3. Equations of motion 855.4. Controller design 865.4.1. Hover control 865.4.2. Transition maneuver control 965.4.3. Horizontal flight control 1025.5. Embedded system 1065.5.1. Experimental platform 1065.5.2. Microcontroller 1085.5.3. Inertial measurement unit (IMU) 1095.5.4. Sensor fusion 1095.6. Conclusions and future works 1115.6.1. Conclusions 1115.6.2. Future works 1125.7. Bibliography 112Chapter 6. Control of Different UAVs with Tilting Rotors 115Juan ESCARENO, Anand SANCHEZ and Octavio GARCIA6.1. Introduction 1156.2. Dynamic model of a flying VTOL vehicle 1166.2.1. Kinematics 1176.2.2. Dynamics 1186.3. Attitude control of a flying VTOL vehicle 1196.4. Triple tilting rotor rotorcraft: Delta 1196.4.1. Kinetics of Delta 1206.4.2. Torques acting on the Delta 1216.4.3. Experimental setup 1236.4.4. Experimental results 1256.5. Single tilting rotor rotorcraft: T-Plane 1276.5.1. Forces and torques acting on the vehicle 1276.5.2. Experimental results 1296.6. Concluding remarks 1316.7. Bibliography 132Chapter 7. Improving Attitude Stabilization of a Quad-Rotor UsingMotor Current Feedback 133Anand SANCHEZ, Luis GARCIA-CARRILLO, Eduardo RONDON and Octavio GARCIA7.1. Introduction 1337.2. Brushless DC motor and speed controller 1347.3. Quad-rotor 1387.3.1. Dynamic model 1397.4. Control strategy 1407.4.1. Attitude control 1407.4.2. Armature current control 1427.5. System configuration 1447.5.1. Aerial vehicle 1457.5.2. Ground station 1467.5.3. Vision system 1477.6. Experimental results 1487.7. Concluding remarks 1507.8. Bibliography 151Chapter 8. Robust Control Design Techniques Applied toMini-Rotorcraft UAV: Simulation and Experimental Results 153José Alfredo GUERRERO, Gerardo ROMERO, Rogelio LOZANO and Efraín ALCORTA8.1. Introduction 1538.2. Dynamic model 1558.3. Problem statement 1568.4. Robust control design 1588.5. Simulation and experimental results 1608.5.1. Simulations 1608.5.2. Experimental platform 1628.6. Conclusions 1648.7. Bibliography 164Chapter 9. Hover Stabilization of a Quad-Rotor Using a Single Camera 167Hugo ROMERO and Sergio SALAZAR9.1. Introduction 1679.2. Visual servoing 1689.2.1. Direct visual servoing 1699.2.2. Indirect visual servoing 1699.2.3. Position based visual servoing 1709.2.4. Image-based visual servoing 1719.2.5.Position-image visual servoing 1729.3. Camera calibration 1739.3.1. Two-plane calibration approach 1739.3.2. Homogenous transformation approach 1759.4. Pose estimation 1779.4.1. Perspective of n-points approach 1779.4.2. Plane-pose-based approach 1799.5. Dynamic model and control strategy 1819.6. Platform architecture 1839.7. Experimental results 1849.7.1. Camera calibration results 1859.7.2. Testing phase 1859.7.3. Real-time results 1859.8. Discussion and conclusions 1869.9. Bibliography 188Chapter 10. Vision-Based Position Control of a Two-Rotor VTOL Mini UAV 191Eduardo RONDON, Sergio SALAZAR, Juan ESCARENO and Rogelio LOZANO10.1. Introduction 19110.2. Position and velocity estimation 19310.2.1. Inertial sensors 19310.2.2. Visual sensors 19310.2.3. Kalman-based sensor fusion 19810.3. Dynamic model 20010.4. Control strategy 20310.4.1. Frontal subsystem (Scamy) 20310.4.2. Lateral subsystem (Scamx) 20410.4.3. Heading subsystem (Sψ) 20410.5. Experimental test bed and results 20410.5.1. Experimental results 20610.6. Concluding remarks 20710.7. Bibliography 207Chapter 11. Optic Flow-Based Vision System for Autonomous 3D Localization and Control of Small Aerial Vehicles 209Farid KENDOUL, Isabelle FANTONI and Kenzo NONAMI11.1. Introduction 20911.2. Related work and the proposed 3NKF framework 21011.2.1. Optic flow computation 21011.2.2.Structure from motion problem 21211.2.3. Bioinspired vision-based aerial navigation 21311.2.4. Brief description of the proposed framework 21311.3. Prediction-based algorithm with adaptive patch for accurate and efficient opticflowcalculation 21511.3.1. Search center prediction 21511.3.2. Combined block-matching and differential algorithm 21611.4. Optic flow interpretation for UAV 3D motion estimation and obstacles detection (SFMproblem) 21911.4.1. Imaging model 21911.4.2. Fusion of OF and angular rate data 22011.4.3. EKF-based algorithm for motion and structure estimation 22111.5. Aerial platform description and real-time implementation 22311.5.1. Quadrotor-based aerial platform 22311.5.2. Real-time software 22511.6. 3D flight tests and experimental results 22711.6.1. Experimental methodology and safety procedures 22711.6.2. Optic flow-based velocity control 22711.6.3. Optic flow-based position control 22911.6.4. Fully autonomous indoor flight using optic flow 23111.7. Conclusion and future work 23311.8. Bibliography 234Chapter 12. Real-Time Stabilization of an Eight-Rotor UAV Using Stereo Vision and Optical Flow 237Hugo ROMERO, Sergio SALAZAR and José GÓMEZ12.1. Stereo vision 23812.2. 3D construction 24212.3. Keypoints matching algorithm 24512.4. Optical flow-based control 24512.4.1. Lucas-Kanade approach 24712.5. Eight-rotorUAV 24912.5.1. Dynamic model 24912.5.2. Control strategy 25712.6. System concept 25912.7. Real-time experiments 26012.8. Bibliography 263Chapter 13. Three-Dimensional Localization 265Juan Gerardo CASTREJON-LOZANO and Alejandro DZUL13.1. Kalman filters 26613.1.1. Linear Kalman filter 26613.1.2. Extended Kalman filter 26913.1.3. Unscented Kalman filter 27013.1.4. Spherical simplex sigma-point Kalman filters 27813.2. Robot localization 28513.2.1. Types of localization 28513.2.2. Inertial navigation theoretical framework 28613.3. Simulations 28913.3.1.Quad-rotorhelicopter 28913.3.2. Inertial navigation simulations 29013.3.3. Conclusions 29613.4. Bibliography 297Chapter 14. Updated Flight Plan for an Autonomous Aircraft in a Windy Environment 301Yasmina BESTAOUI and Fouzia LAKHLEF14.1. Introduction 30114.2. Modeling 30414.2.1. Down-draftmodeling 30414.2.2. Translational dynamics 30514.3. Updated flight planning30814.3.1. Basic problem statement 31014.3.2. Hierarchical planning structure 31114.4. Updates of the reference trajectories: time optimal problem 31214.5. Analysis of the first set of solutionsS1 31514.6. Conclusions 32314.7. Bibliography 323List of Authors 327Index 331