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Indoor Navigation Strategies for Aerial Autonomous Systems presents the necessary and sufficient theoretical basis for those interested in working in unmanned aerial vehicles, providing three different approaches to mathematically represent the dynamics of an aerial vehicle.
The book contains detailed information on fusion inertial measurements for orientation stabilization and its validation in flight tests, also proposing substantial theoretical and practical validation for improving the dropped or noised signals. In addition, the book contains different strategies to control and navigate aerial systems.
The comprehensive information will be of interest to both researchers and practitioners working in automatic control, mechatronics, robotics, and UAVs, helping them improve research and motivating them to build a test-bed for future projects.
- Provides substantial information on nonlinear control approaches and their validation in flight tests
- Details in observer-delay schemes that can be applied in real-time
- Teaches how an IMU is built and how they can improve the performance of their system when applying observers or predictors
- Improves prototypes with tactics for proposed nonlinear schemes
Academics and researches in automatic control, mechatronics, robotics, Unmanned Aerial Vehicles (UAVs)
- About the Authors
- Part I: Background
- Chapter 1: State-of-the-Art
- 1.1. Mathematical Representation of the Vehicle Dynamics
- 1.2. Attitude Estimation Using Inertial Sensors
- 1.3. Delay Systems & Predictors
- 1.4. Data Fusion for UAV Localization
- 1.5. Control & Navigation Algorithms
- 1.6. Trajectory Generation & Tracking
- 1.7. Obstacle Avoidance
- 1.8. Teleoperation
- Chapter 2: Modeling Approaches
- 2.1. Force and Moment in a Rotor
- 2.2. Euler–Lagrange Approach
- 2.3. Newton–Euler Approach
- 2.4. Quaternion Approach
- 2.5. Discussion
- Part II: Improving Sensor Signals for Control Purposes
- Improving Sensor Signals for Control Purposes
- Chapter 3: Inertial Sensors Data Fusion for Orientation Estimation
- 3.1. Attitude Representation
- 3.2. Sensor Characterization
- 3.3. Attitude Estimation Algorithms
- 3.4. A Computationally-Efficient Kalman Filter
- 3.5. Discussion
- Chapter 4: Delay Signals & Predictors
- 4.1. Observer–Predictor Algorithm for Compensation of Measurement Delays
- 4.2. State Predictor–Control Scheme
- 4.3. Discussion
- Chapter 5: Data Fusion for UAV Localization
- 5.1. Sensor Data Fusion
- 5.2. Prototype and Numerical Implementation
- 5.3. Flight Tests and Experimental Results
- 5.4. OptiTrack Measurements vs EKF Estimation
- 5.5. Rotational Optical Flow Compensation
- 5.6. Discussion
- Part III: Navigation Schemes & Control Strategies
- Navigation Schemes & Control Strategies
- Chapter 6: Nonlinear Control Algorithms with Integral Action
- 6.1. From PD to PID Controllers
- 6.2. Saturated Controllers with Integral Component
- 6.3. Integral and Adaptive Backstepping Control – IAB
- 6.4. Discussion
- Chapter 7: Sliding Mode Control
- 7.1. From the Nonlinear Attitude Representation to Linear MIMO Expression
- 7.2. Nonlinear Optimal Controller with Integral Sliding Mode Design
- 7.3. Numerical Validation
- 7.4. Real-Time Validation
- 7.5. Discussion
- Chapter 8: Robust Simple Controllers
- 8.1. Nonlinear Robust Algorithms Based on Saturation Functions
- 8.2. Robust Control Based on an Uncertainty Estimator
- 8.3. Discussion
- Chapter 9: Trajectory Generation, Planning & Tracking
- 9.1. Quadrotor Mathematical Description
- 9.2. Time-Optimal Trajectory Generation
- 9.3. UAV Routing Problem for Inspection-Like Missions
- 9.4. Trajectory Tracking Problem
- 9.5. Simulation Results
- 9.6. Discussion
- Chapter 10: Obstacle Avoidance
- 10.1. Artificial Potential Field Method
- 10.2. Obstacle Avoidance Algorithm
- 10.3. Limit-Cycle Obstacle Avoidance
- 10.4. Discussion
- Chapter 11: Haptic Teleoperation
- 11.1. Experimental Setup
- 11.2. Collision Avoidance
- 11.3. Haptic Teleoperation
- 11.4. Real-Time Experiments
- 11.5. Discussion
- No. of pages:
- © Butterworth-Heinemann 2017
- 11th November 2016
- Paperback ISBN:
- eBook ISBN:
He received the best Ph.D. thesis of Automatic Control award from club EEA, (France) in 2005. His research topics cover: real-time control applications, non-linear dynamics and control, aerospace vehicles, vision and underactuated mechanical systems.
Researcher, French National Research Foundation (CNRS), Laboratory Heudiasyc,University of Technology of Compiegne, France
She obtained her B.S degree in Electronics and Telecommunications Engineering in 2005 and her M.Sc degree in Automation and Control in 2007 from the Hidalgo State University, Mexico. In 2009 she obtained her Ph.D. degree in Automatic Control from the University of Technology of Compiègne, France.
Engineer in research and development in a Start-Up in France
He has been visiting researcher at the Lund Institute of Technology, Lund, Sweden (in 2006), Université de Technologie de Compiegne, Compiegne, France (in 2007), University of Florianopolis, Brazil (in 2010), and at the University of Sheffield (UK) (in 2014). He has co-authored more than 15 papers in middle or top impact journals. His research interests are within the broad area of time delay systems, embedded control systems and control of autonomous vehicles.
Assistant Professor of Automatic Control, Technical University of Valencia, Spain
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