Small Unmanned Aircraft Theory And Practice Pdf

[Fleur Francour]

Autonomousunmanned air vehicles (UAVs) are critical to current and future military, civil, and commercial operations. Despite their importance, no previous textbook has accessibly introduced UAVs to students in the engineering, computer, and science disciplines--until now. Small Unmanned Aircraft provides a concise but comprehensive description of the key concepts and technologies underlying the dynamics, control, and guidance of fixed-wing unmanned aircraft, and enables all students with an introductory-level background in controls or robotics to enter this exciting and important area.

The authors explore the essential underlying physics and sensors of UAV problems, including low-level autopilot for stability and higher-level autopilot functions of path planning. The textbook leads the student from rigid-body dynamics through aerodynamics, stability augmentation, and state estimation using onboard sensors, to maneuvering through obstacles. To facilitate understanding, the authors have replaced traditional homework assignments with a simulation project using the MATLAB/Simulink environment. Students begin by modeling rigid-body dynamics, then add aerodynamics and sensor models. They develop low-level autopilot code, extended Kalman filters for state estimation, path-following routines, and high-level path-planning algorithms. The final chapter of the book focuses on UAV guidance using machine vision.

"This book presents a unique and broad introduction to the necessary background, tools, and methods to design guidance, navigation, and control systems for unmanned air vehicles. Written with confidence and authority by leading researchers in the field, this effectively organized book provides an excellent reference for all those interested in this subject."--Emilio Frazzoli, Massachusetts Institute of Technology

"Presenting aircraft dynamics to nonaerospace students, this book provides a clear description and explanation for the design of navigation, guidance, and control algorithms for small to miniature unmanned aircraft systems."--Eric W. Frew, University of Colorado, Boulder

Randal W. Beard is a professor in the Department of Electrical and Computer Engineering at Brigham Young University. He is the coauthor of Distributed Consensus in Multi-vehicle Cooperative Control.

Timothy W. McLain is a professor in the Department of Mechanical Engineering at Brigham Young University.

Attitude of a multicopter while flying at constant speed or while hovering under the influence of horizontal atmospheric wind. The multicopter tilts (Γ) toward the wind direction so that it can balance the aerodynamic force (Faero).

Maps describing the atmospheric conditions (surface wind speed, direction, temperature, and pressure) at Poltringen airfield (48.545N, 8.947E; red dot) on the day of the calibration flights, 23 Feb 2021. The four plots describe the evolution of these parameters from (top left) 1400 UTC to (bottom right) 1700 UTC. The plots were generated using ERA5 with a 9 km grid resolution. Below the maps, a timeline shows when the four flights have been performed with respect to the four weather maps provided.

(a) The red points mark the tilt angle data of Eq. (34) (after the postprocessing wind disturbance correction where GS = TAS). The blue line represents a third-order polynomial fit from (35) between the tilt angle and horizontal wind velocity. (b) The points mark the extended drag coefficient data of Eq. (33) (after the postprocessing wind disturbance correction where GS = TAS). The color map represents the true airspeed. The blue line represents the exponential decay of the CA model in (36).

Comparison between the horizontal wind vector detected by the ultrasonic anemometer (black) and by the multicopter. The plots show around 300 s of hovering at 90 m altitude for flight 7. All the time series have been resampled to 0.2 Hz for the comparison. (a),(b) Wind magnitude obtained with the two models using the UAS tilt angle (green and red). (c) Horizontal wind direction.

Comparison between the horizontal wind vector detected by the ultrasonic anemometer and by the multicopter throughout all the eight validation flights. (a) Wind magnitude obtained with the direct model. (b) Wind magnitude obtained with the CA model.

Comparison of the tilt angle of two DJI S900, one with the sphere and the other one without. In total, 3 h of hovering are plotted with the tilt angle of the UAS with the dome ranging from 3 to 19.5. The red line represent the linear fit of the cloud of points.

Comparison between the horizontal wind vector detected by the ultrasonic anemometer (black) and by the multicopter. The plots show all the available hovering data at 50 m altitude. All the time series have been resampled to 0.2 Hz for the comparison. (a),(b) Wind magnitude obtained with the two models using the UAS tilt angle (green and red). (c) Horizontal wind direction.

Comparison between the horizontal wind vector detected by the ultrasonic anemometer (black) and by the multicopter. The plots show all the available hovering data at 90 m altitude. All the time series have been resampled to 0.2 Hz for the comparison. (a),(b) Wind magnitude obtained with the two models using the UAS tilt angle (green and red). (c) Horizontal wind direction.

2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Wind field measurements in the lower atmospheric boundary layer are important for meteorological and industrial applications (Chioncel et al. 2011; Platis et al. 2020, 2021). Ultrasonic anemometers and cup anemometers, commonly used for weather forecasting, require mounting on a mast for single-point measurement. Lidar and sodar, on the other hand, offer remote sensing capabilities but have lower resolution and higher maintenance needs. All of these wind field measurement methods have limitations in terms of applicability, measurement accuracy, and cost.

Rotary-wing uncrewed aircraft systems (UAS), also known as multicopters, offer a cost-effective and flexible solution for measuring the horizontal wind vector (Barbieri et al. 2019). These systems can take off and land vertically, hover at a fixed point, and fly autonomously without prior knowledge of the wind field (Waslander and Wang 2009). In addition, several integrated sensors can be mounted on a multicopter to provide temperature, pressure, humidity, and CO2 measurements (Segales et al. 2020; Bell et al. 2020; Varentsov et al. 2021). Previous studies have demonstrated the use of data gathered by frequent automated profiling to improve regional and mesoscale numerical weather prediction models (Chilson et al. 2019). Targeted observations can also be quickly scheduled to enhance the prediction of extreme weather events (Pinto et al. 2021). Despite limitations in flight endurance, automatic landing procedures and self-recharging stations can improve the operability of these systems.

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