book

Unmanned Aerial Vehicles: Embedded Control

by Rogelio Lozano

March 2010

Intermediate to advanced

352 pages

7h 35m

English

Wiley

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1.1. Aerodynamic configurations1.2. Dynamic models1.2.1. Newton-Euler approach1.2.2. Euler-Lagrange approach1.2.3. Quaternion approach1.2.4. Example: dynamic model of a quad-rotor rotorcraft1.3. Bibliography
2.1. Introduction2.2. Bibliographical study2.3. The PVTOL aircraft model2.4. Control strategy2.4.1. Control of the vertical displacement y2.4.2. Control of the roll angle θ and the horizontal displacement x2.4.2.1. Boundedness of θ2.4.2.2. Boundedness of θ2.4.2.3. Boundedness of x2.4.2.4. Boundedness of x2.4.2.5. Convergence of θ, θ, x and x to zero2.5. Other control strategies for the stabilization of the PVTOL aircraft2.6. Experimental results2.7. Conclusions2.8. Bibliography
3.1. Introduction3.2. Dynamic model3.2.1. Kinematics3.2.2. Dynamics3.2.2.1. Forces acting on the vehicle3.2.2.2. Torques acting on the vehicle3.2.3. Model for control analysis3.3. Control strategy3.3.1. Altitude control3.3.2. Horizontal motion control3.3.3. Attitude control3.4. Experimental setup3.4.1. Onboard flight system (OFS)3.4.2. Outboard visual system3.4.2.1. Position3.4.2.2. Optical flow3.4.3. Experimental results3.5. Concluding remarks3.6. Bibliography
4.1. Introduction4.2. Two-rotor UAV4.2.1. Description4.2.2. Dynamic model4.2.2.1. Translational motion4.2.2.2. Rotational motion4.2.2.3. Reduced model4.3. Control algorithm design4.4. Experimental platform4.4.1. Real-time PC-control system (PCCS)4.4.1.1. Sensors and communication hardware4.4.2. Experimental results4.5. Conclusion4.6. Bibliography
5.1. Introduction5.2. Convertible plane UAV5.2.1. Vertical mode5.2.2. Transition maneuver5.2.3. Horizontal mode5.3. Mathematical model5.3.1. Translation of the vehicle5.3.2. Orientation of the vehicle5.3.2.1. Euler angles5.3.2.2. Aerodynamic axes5.3.2.3. Torques5.3.3. Equations of motion5.4. Controller design5.4.1. Hover control5.4.1.1. Axial system5.4.1.2. Longitudinal system5.4.1.3. Lateral system5.4.1.4. Simulation and experimental results5.4.2. Transition maneuver control5.4.3. Horizontal flight control5.5. Embedded system5.5.1. Experimental platform5.5.2. Microcontroller5.5.3. Inertial measurement unit (IMU)5.5.4. Sensor fusion5.6. Conclusions and future works5.6.1. Conclusions5.6.2. Future works5.7. Bibliography
6.1. Introduction6.2. Dynamic model of a flying VTOL vehicle6.2.1. Kinematics6.2.2. Dynamics6.3. Attitude control of a flying VTOL vehicle6.4. Triple tilting rotor rotorcraft: Delta6.4.1. Kinetics of Delta6.4.2. Torques acting on the Delta6.4.3. Experimental setup6.4.3.1. Avionics6.4.3.2. Sensor module (SM)6.4.3.3. On-board microcontroller (OBM)6.4.3.4. Data acquisition module (DAQ)6.4.4. Experimental results6.5. Single tilting rotor rotorcraft: T-Plane6.5.1. Forces and torques acting on the vehicle6.5.2. Experimental results6.5.2.1. Experimental platform6.5.2.2. Experimental test6.6. Concluding remarks6.7. Bibliography
7.1. Introduction7.2. Brushless DC motor and speed controller7.3. Quad-rotor7.3.1. Dynamic model7.4. Control strategy7.4.1. Attitude control7.4.2. Armature current control7.5. System configuration7.5.1. Aerial vehicle7.5.2. Ground station7.5.3. Vision system7.6. Experimental results7.7. Concluding remarks7.8. Bibliography

8.1. Introduction8.2. Dynamic model8.3. Problem statement8.4. Robust control design8.5. Simulation and experimental results8.5.1. Simulations8.5.2. Experimental platform8.6. Conclusions8.7. Bibliography
9.1. Introduction9.2. Visual servoing9.2.1. Direct visual servoing9.2.2. Indirectvisual servoing9.2.3. Position based visual servoing9.2.4. Image-basedvisual servoing9.2.5. Position-image visual servoing9.3. Camera calibration9.3.1. Two-plane calibration approach9.3.2. Homogenous transformation approach9.4. Pose estimation9.4.1. Perspective of n-points approach9.4.2. Plane-pose-based approach9.5. Dynamic model and control strategy9.6. Platform architecture9.7. Experimental results9.7.1. Camera calibration results9.7.2. Testing phase9.7.3. Real-time results9.8. Discussion and conclusions9.9. Bibliography
10.1. Introduction10.2. Position and velocity estimation10.2.1. Inertial sensors10.2.2. Visual sensors10.2.2.1. Position10.2.2.2. Optical flow (OF)10.2.3. Kalman-based sensor fusion10.3. Dynamic model10.4. Control strategy10.4.1. Frontal subsystem (Scamy)10.4.2. Lateral subsystem (Scamx)10.4.3. Heading subsystem (Sψ)10.5. Experimental testbed and results10.5.1. Experimental results10.6. Concluding remarks10.7. Bibliography
11.1. Introduction11.2. Related work and the proposed 3NKF framework11.2.1. Optic flow computation11.2.2. Structure from motion problem11.2.3. Bioinspired vision-based aerial navigation11.2.4. Brief description of the proposed framework11.3. Prediction-based algorithm with adaptive patch for accurate and efficient optic flow calculation11.3.1. Search center prediction11.3.2. Combined block-matching and differential algorithm11.3.2.1. 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 model11.4.2. Fusion of OF and angular rate data11.4.3. EKF-based algorithm for motion and structure estimation11.5. Aerial platform description and real-time implementation11.5.1. Quadrotor-based aerial platform11.5.2. Real-time software11.6. 3D flight tests and experimental results11.6.1. Experimental methodology and safety procedures11.6.2. Optic flow-based velocity control11.6.3. Optic flow-based position control11.6.4. Fully autonomous indoor flight using optic flow11.7. Conclusion and future work11.8. Bibliography
12.1. Stereo vision12.2. 3D reconstruction12.3. Keypoints matching algorithm12.4. Optical flow-based control12.4.1. Lucas-Kanade approach12.5. Eight-rotor UAV12.5.1. Dynamic model12.5.1.1. Translational subsystem model12.5.1.2. Rotational subsystem model12.5.2. Control strategy12.5.2.1. Attitude control12.5.2.2. Horizontal displacements and altitude control12.6. System concept12.7. Real-time experiments12.8. Bibliography
13.1. Kalman filters13.1.1. Linear Kalman filter13.1.2. Extended Kalman filter13.1.3. Unscented Kalman filter13.1.3.1. UKF algorithm13.1.3.2. Additive UKF algorithm13.1.3.3. Square-root UKF algorithm13.1.3.4. Additive square-root UKF algorithm13.1.4. Spherical simplex sigma-point Kalman filters13.1.4.1. Spherical simplex sigma-point approach13.1.4.2. Spherical simplex UKF algorithm13.1.4.3. Additive SS-UKF Algorithm13.1.4.4. Square-root SS-UKF algorithm13.1.4.5. Square-root additive SS-UKF algorithm13.2. Robot localization13.2.1. Types of localization13.2.1.1. Dead reckoning (navigation systems)13.2.1.2. A priori map-based localization13.2.1.3. Simultaneous localization and mapping (SLAM)13.2.2. Inertial navigation theoretical framework13.2.2.1. Navigation equations in the navigation frame13.3. Simulations13.3.1. Quad-rotor helicopter13.3.2. Inertial navigation simulations13.3.3. Conclusions13.4. Bibliography
14.1. Introduction14.2. Modeling14.2.1. Down-draft modeling14.2.2. Translational dynamics14.3. Updated flight planning14.3.1. Basic problem statement14.3.2. Hierarchical planning structure14.4. Updates of the reference trajectories: time optimal problem14.5. Analysis of the first set of solutions S114.6. Conclusions14.7. Bibliography

Overview

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.