Unmanned Aerial Vehicles: Embedded Control

Unmanned Aerial Vehicles: Embedded Control

by Rogelio Lozano
Released March 2010
Publisher(s): Wiley
ISBN: 9781848211278

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Book description

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.

Table of contents

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

Product information

  • Title: Unmanned Aerial Vehicles: Embedded Control
  • Author(s): Rogelio Lozano
  • Release date: March 2010
  • Publisher(s): Wiley
  • ISBN: 9781848211278