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Relevant bibliographies by topics / Micro-UAV control

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Academic literature on the topic 'Micro-UAV control'

Author: Grafiati

Published: 4 June 2021

Last updated: 11 February 2022

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Journal articles on the topic "Micro-UAV control"

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Gabrlik, Petr, Vlastimil Kriz, and Ludek Zalud. "RECONNAISSANCE MICRO UAV SYSTEM." Acta Polytechnica CTU Proceedings 2, no. 2 (December 31, 2015): 15–21. http://dx.doi.org/10.14311/app.2015.1.0015.

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This paper describes the design and implementation of the Uranus UAV. This quad-rotor flying robot was created to extend the abilities of the hitherto developed with airborne missions. The first part deals with the mathematical model of the robot. Next, the control system is designed, and the proposed hardware as well as the implemented software solution are presented. For integration into the robotic system, a new communication protocol was created and is described here too.

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Zhai, Rui Yong, Wen Dong Zhang, Zhao Ying Zhou, Sheng Bo Sang, and Pei Wei Li. "Trajectory Tracking Control for Micro Unmanned Aerial Vehicles." Advanced Materials Research 798-799 (September 2013): 448–51. http://dx.doi.org/10.4028/www.scientific.net/amr.798-799.448.

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This article considers the problem of trajectory tracking control for a micro fixed-wing unmanned air vehicle (UAV). With Bank-to-Turn (BTT) method to manage lateral deviation control of UAV, this paper discusses the outer loop guidance system, which separates the vehicle guidance problems into lateral control loop and longitudinal control loop. Based on the kinematic model of the coordinated turning of UAV, the aircraft can track a pre-specified flight path with desired error range. Flight test results on a fixed-wing UAV have indicated that the trajectory tracking control law is quite effective.

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Kojima, Ryota, Kakuji Ogawara, and Takahiro Yoneda. "1307 Delayed Feedback Altitude Control for Micro UAV." Proceedings of Conference of Chugoku-Shikoku Branch 2009.47 (2009): 439–40. http://dx.doi.org/10.1299/jsmecs.2009.47.439.

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Michael, Nathan, Davide Scaramuzza, and Vijay Kumar. "Special issue on micro-UAV perception and control." Autonomous Robots 33, no. 1-2 (May 5, 2012): 1–3. http://dx.doi.org/10.1007/s10514-012-9295-y.

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Zhang, Hong Jun, Lu Wen Jun, and Li Biao Tong. "Design and Implementation of a Detection Device for Flight Control System in Unmanned Aerial Vehicle." Applied Mechanics and Materials 121-126 (October 2011): 764–67. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.764.

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Flight control system (FCS) is the command center for the unmanned aerial vehicle (UAV). A low-cost, high-precision micro-UAV attitude calibration table is designed by utilizing the structure of the vertical gyroscope of the flight attitude angle sensor. The detection device for the UAV FCS developed by loop-in-simulation achieves unmanned attitude calibration and overall performance detection of the FCS.

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Xiong, Wei, Zhao Ying Zhou, and Xiao Yan Liu. "Study of Low Cost Micro Autopilot for Fixed-Wing UAV." Advanced Materials Research 317-319 (August 2011): 1672–76. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.1672.

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From the cost-effective viewpoint of low cost Bank-to-Turn (BTT) Unmanned Air Vehicles (UAV) and target drone, a low cost flight control system, with the fewest number of sensors, is studied in this paper for the fixed-wing UAV. The structure of the control system is described which is able to estimate necessary information to provide stabilization and guidance for a small fixed wing BTT UAV. The practical flight control system structure and control law for roll hold loop, altitude hold loop, trajectory tracking loop are designed based on the sensor configuration with only a MEMS rate gyro, a MEMS pressure sensor and global positioning system (GPS) receiver only. A prototype low cost autopilot is trial-produced to control a typical UAV. The Experimental results show the effectiveness of navigation and control methods of f the proposed methodology.

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Bristeau, Pierre-Jean, François Callou, David Vissière, and Nicolas Petit. "The Navigation and Control technology inside the AR.Drone micro UAV." IFAC Proceedings Volumes 44, no. 1 (January 2011): 1477–84. http://dx.doi.org/10.3182/20110828-6-it-1002.02327.

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Kownacki, Cezary. "REAL FLIGHT DEMONSTRATION OF PITCH AND ROLL CONTROL FOR UAV CANYON FLIGHTS." Acta Mechanica et Automatica 7, no. 3 (September 1, 2013): 148–54. http://dx.doi.org/10.2478/ama-2013-0025.

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Abstract The paper presents results of an experiment prepared to validate the autonomous control of obstacle avoidance designed for a micro UAV to fly in urban canyons. The idea of the obstacle avoidance assumes usage of two miniature laser rangefinders responsible for obstacle detection and range measurement. Measured ranges from obstacles placed on both sides of UAV can be used to simultaneous control of desired roll and pitch angles. Such combination of controls allows achieving high agility of UAV, because during a maneuver of obstacle avoidance UAV can make a turn and climb at the same time. In the experiment, controls of roll and pitch angles were verified separately to ensure high reliability of results and clearance of UAV behavior in the real flight. Because of lack of appropriate objects, which can be used as obstacles, laser rangefinders were directed vertically to the ground instead of the original horizontal configuration. So sensors determine ranges from the ground during a descent flight of UAV, and if their values are lower than defined threshold, it could be interpreted as obstacle detection. The experiment results present UAV behavior adequate to designed controls of roll and pitch angle. The vehicle turns in the opposite direction to the sensing axis of laser rangefinder detecting an obstacle and starts climbing when both sensors detect obstacles at the same range below the threshold.

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Tang, Wei, Lijian Wang, Jiawei Gu, and Yunfeng Gu. "Single Neural Adaptive PID Control for Small UAV Micro-Turbojet Engine." Sensors 20, no. 2 (January 8, 2020): 345. http://dx.doi.org/10.3390/s20020345.

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The micro-turbojet engine (MTE) is especially suitable for unmanned aerial vehicles (UAVs). Because the rotor speed is proportional to the thrust force, the accurate speed tracking control is indispensable for MTE. Thanks to its simplicity, the proportional–integral–derivative (PID) controller is commonly used for rotor speed regulation. However, the PID controller cannot guarantee superior performance over the entire operation range due to the time-variance and strong nonlinearity of MTE. The gain scheduling approach using a family of linear controllers is recognized as an efficient alternative, but such a solution heavily relies on the model sets and pre-knowledge. To tackle such challenges, a single neural adaptive PID (SNA-PID) controller is proposed herein for rotor speed control. The new controller featuring with a single-neuron network is able to adaptively tune the gains (weights) online. The simple structure of the controller reduces the computational load and facilitates the algorithm implementation on low-cost hardware. Finally, the proposed controller is validated by numerical simulations and experiments on the MTE in laboratory conditions, and the results show that the proposed controller achieves remarkable effectiveness for speed tracking control. In comparison with the PID controller, the proposed controller yields 54% and 66% reductions on static tracking error under two typical cases.

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Passafiume, Marco, Neda Rojhani, Giovanni Collodi, and Alessandro Cidronali. "Modeling Small UAV Micro-Doppler Signature Using Millimeter-Wave FMCW Radar." Electronics 10, no. 6 (March 22, 2021): 747. http://dx.doi.org/10.3390/electronics10060747.

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With the increase in small unmanned aerial vehicle (UAV) applications in several technology areas, detection and small UAVs classification have become of interest. To cope with small radar cross-sections (RCSs), slow-flying speeds, and low flying altitudes, the micro-Doppler signature provides some of the most distinctive information to identify and classify targets in many radar systems. In this paper, we introduce an effective model for the micro-Doppler effect that is suitable for frequency-modulated continuous-wave (FMCW) radar applications, and exploit it to investigate UAV signatures. The latter depends on the number of UAV motors, which are considered vibrational sources, and their rotation speed. To demonstrate the reliability of the proposed model, it is used to build simulated FMCW radar images, which are compared with experimental data acquired by a 77 GHz FMCW multiple-input multiple-output (MIMO) cost-effective automotive radar platform. The experimental results confirm the model’s ability to estimate the class of the UAV, namely its number of motors, in different operative scenarios. In addition, the experimental results show that the motors rotation speed does not imprint a significant signature on the classification of the UAV; thus, the estimation of the number of motors represents the only viable parameter for small UAV classification using the micro-Doppler effect.

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Dissertations / Theses on the topic "Micro-UAV control"

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Fowers, Spencer G. "Stabilization and Control of a Quad-Rotor Micro-UAV Using Vision Sensors." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2375.pdf.

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Tippetts, Beau J. "Real-time implementations of vision algorithms for control, stabilization, and target tracking, for a hovering micro-UAV /." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2374.pdf.

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Tippetts, Beau J. "Real-Time Implementation of Vision Algorithm for Control, Stabilization, and Target Tracking for a Hovering Micro-UAV." BYU ScholarsArchive, 2008. https://scholarsarchive.byu.edu/etd/1418.

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A lightweight, powerful, yet efficient quad-rotor platform was designed and constructed to obtain experimental results of completely autonomous control of a hovering micro-UAV using a complete on-board vision system. The on-board vision and control system is composed of a Helios FPGA board, an Autonomous Vehicle Toolkit daughterboard, and a Kestrel Autopilot. The resulting platform is referred to as the Helio-copter. An efficient algorithm to detect, correlate, and track features in a scene and estimate attitude information was implemented with a combination of hardware and software on the FPGA, and real-time performance was obtained. The algorithms implemented include a Harris feature detector, template matching feature correlator, RANSAC similarity-constrained homography, color segmentation, radial distortion correction, and an extended Kalman filter with a standard-deviation outlier rejection technique (SORT). This implementation was designed specifically for use as an on-board vision solution in determining movement of small unmanned air vehicles that have size, weight, and power limitations. Experimental results show the Helio-copter capable of maintaining level, stable flight within a 6 foot by 6 foot area for over 40 seconds without human intervention.

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Hayes, Edwin Laurie. "Machine Learning for Intelligent Control: Application of Reinforcement Learning Techniques to the Development of Flight Control Systems for Miniature UAV Rotorcraft." Thesis, University of Canterbury. Department of Mechanical Engineering, 2013. http://hdl.handle.net/10092/7810.

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This thesis investigates the possibility of using reinforcement learning (RL) techniques to create a flight controller for a quadrotor Micro Aerial Vehicle (MAV). A capable flight control system is a core requirement of any unmanned aerial vehicle. The challenging and diverse applications in which MAVs are destined to be used, mean that considerable time and effort need to be put into designing and commissioning suitable flight controllers. It is proposed that reinforcement learning, a subset of machine learning, could be used to address some of the practical difficulties. While much research has delved into RL in unmanned aerial vehicle applications, this work has tended to ignore low level motion control, or been concerned only in off-line learning regimes. This thesis addresses an area in which accessible information is scarce: the performance of RL when used for on-policy motion control. Trying out a candidate algorithm on a real MAV is a simple but expensive proposition. In place of such an approach, this research details the development of a suitable simulator environment, in which a prototype controller might be evaluated. Then inquiry then proposes a possible RL-based control system, utilising the Q-learning algorithm, with an adaptive RBF-network providing function approximation. The operation of this prototypical control system is then tested in detail, to determine both the absolute level of performance which can be expected, and the effect which tuning critical parameters of the algorithm has on the functioning of the controller. Performance is compared against a conventional PID controller to maximise the usability of the results by a wide audience. Testing considers behaviour in the presence of disturbances, and run-time changes in plant dynamics. Results show that given sufficient learning opportunity, a RL-based control system performs as well as a simple PID controller. However, unstable behaviour during learning is an issue for future analysis. Additionally, preliminary testing is performed to evaluate the feasibility of implementing RL algorithms in an embedded computing environment, as a general requirement for a MAV flight controller. Whilst the algorithm runs successfully in an embedded context, observation reveals further development would be necessary to reduce computation time to a level where a controller was able to update sufficiently quickly for a real-time motion control application. In summary, the study provides a critical assessment of the feasibility of using RL algorithms for motion control tasks, such as MAV flight control. Advantages which merit interest are exposed, though practical considerations suggest at this stage, that such a control system is not a realistic proposition. There is a discussion of avenues which may uncover possibilities to surmount these challenges. This investigation will prove useful for engineers interested in the opportunities which reinforcement learning techniques represent.

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Farrell, Michael David. "Error-State Estimation and Control for a Multirotor UAV Landing on a Moving Vehicle." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/7879.

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Though multirotor unmanned aerial vehicles (UAVs) have become widely used during the past decade, challenges in autonomy have prevented their widespread use when moving vehicles act as their base stations. Emerging use cases, including maritime surveillance, package delivery and convoy support, require UAVs to autonomously operate in this scenario. This thesis presents improved solutions to both the state estimation and control problems that must be solved to enable robust, autonomous landing of multirotor UAVs onto moving vehicles.Current state-of-the-art UAV landing systems depend on the detection of visual fiducial markers placed on the landing target vehicle. However, in challenging conditions, such as poor lighting, occlusion, or extreme motion, these fiducial markers may be undected for significant periods of time. This thesis demonstrates a state estimation algorithm that tracks and estimates the locations of unknown visual features on the target vehicle. Experimental results show that this method significantly improves the estimation of the state of the target vehicle while the fiducial marker is not detected.This thesis also describes an improved control scheme that enables a multirotor UAV to accurately track a time-dependent trajectory. Rooted in Lie theory, this controller computes the optimal control signal based on an error-state formulation of the UAV dynamics. Simulation and hardware experiments of this control scheme show its accuracy and computational efficiency, making it a viable solution for use in a robust landing system.

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Ohanian, Osgar John. "Ducted Fan Aerodynamics and Modeling, with Applications of Steady and Synthetic Jet Flow Control." Diss., Virginia Tech, 2011. http://hdl.handle.net/10919/27687.

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Ducted fan vehicles possess a superior ability to maximize payload capacity while minimizing vehicle size. Their ability to both hover and fly at high speed is a key advantage for information-gathering missions, particularly when close proximity to a target is essential. However, the ducted fanâ s aerodynamic characteristics pose difficulties for stable vehicle flight and therefore require complex control algorithms. In particular, they exhibit a large nose-up pitching moment during wind gusts and when transitioning from hover to forward flight. Understanding ducted fan aerodynamic behavior and how it can be altered through flow control techniques are the two prime objectives of this work. This dissertation provides a new paradigm for modeling the ducted fanâ s nonlinear behavior and new methods for changing the duct aerodynamics using active flow control. Steady and piezoelectric synthetic jet blowing are employed in the flow control concepts and are compared. The new aerodynamic model captures the nonlinear characteristics of the force, moment, and power data for a ducted fan, while representing these terms in a set of simple equations. The model attains excellent agreement with current and legacy experimental data using twelve non-dimensional constants. Synthetic jet actuators (SJA) have potential for use in flow control applications in UAVs with limited size, weight, and power budgets. Piezoelectric SJAs for a ducted fan vehicle were developed through two rounds of experimental designs. The final SJA design attained peak jet velocities in the range of 225 ft/sec (69 m/s) for a 0.03â x 0.80â rectangular slot. To reduce the magnitude of the nose-up pitching moment in cross-winds, two flow control concepts were explored: flow separation control at the duct lip, and flow turning at the duct trailing edge using a CoandÄ surface. Both concepts were experimentally proven to be successful. Synthetic jets and steady jets were capable of modifying the ducted fan flow to reduce pitching moment, but some cases required high values of steady blowing to create significant responses. Triggering leading edge separation on the duct lip was one application where synthetic jets showed comparable performance to steady jets operating at a blowing coefficient an order of magnitude higher.
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Smith, David Everett. "Modelling and controlling a bio-inspired flapping-wing micro aerial vehicle." Thesis, Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/43577.

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The objective of this research is to verify the three degree of freedom capabilities of a bio-inspired quad flapping-wing micro aerial vehicle in simulation and in hardware. The simulation employs a nonlinear plant model and input-output feedback linearization controller to verify the three degree of freedom capabilities of the vehicle. The hardware is a carbon fiber test bench with four flapping wings and an embedded avionics system which is controlled via a PD linear controller. Verification of the three degree of freedom capabilities of the quad flapping-wing concept is achieved by analyzing the response of both the simulation and test bench to pitch, roll, and yaw attitude commands.

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Lindqvist, Björn. "Combined Control and Path Planning for a Micro Aerial Vehicle based on Non-linear MPC with Parametric Geometric Constraints." Thesis, Luleå tekniska universitet, Rymdteknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-76212.

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Using robots to navigate through un-mapped environments, specially man-made infrastructures, for the purpose of exploration or inspection is a topic that has gathered a lot of interest in the last years. Micro Aerial Vehicles (MAV's) have the mobility and agility to move quickly and access hard-to-reach areas where ground robots would fail, but using MAV's for that purpose comes with its own set of problems since any collision with the environment results in a crash. The control architecture used in a MAV for such a task needs to perform obstacle avoidance and on-line path-planning in an unknown environment with low computation times as to not lose stability. In this thesis a Non-linear Model Predictive Controller (NMPC) for obstacle avoidance and path-planning on an aerial platform will be established. Included are methods for constraining the available state-space, simulations of various obstacle avoidance scenarios for single and multiple MAVs and experimental validation of the proposed control architecture. The validity of the proposed approach is demonstrated through multiple experimental and simulation results. In these approaches, the positioning information of the obstacles and the MAV are provided by a motion-capture system. The thesis will conclude with the demonstration of an experimental validation of a centralized NMPC for collision avoidance of two MAV's.

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Book chapters on the topic "Micro-UAV control"

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Grondel, Sébastien, Mathieu Colin, Marie Zwingelstein-Colin, Sofiane Ghenna, Caroline Soyer, Eric Cattan, and Olivier Thomas. "Towards the Use of Flapping Wing Nano Aerial Vehicles." In Modern Technologies Enabling Safe and Secure UAV Operation in Urban Airspace. IOS Press, 2021. http://dx.doi.org/10.3233/nicsp210006.

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In the last decade, the potential of Micro Aerial Vehicles (MAVs) has generated an enormous interest in this technology and numerous applications have therefore been proposed in military and civilian fields. More recently, researchers have begun to work on a new and miniaturized generation called Flapping Wing Nano Aerial Vehicles (FWNAVs) who could be particularly promising for the indoor inspection. Before to be able to use efficiently these FWNAVs, there are however significant scientific and technical challenges to solve due to the scaling down. These include aerodynamics of low Reynolds number flow, small-scale power generation and power storage, navigation and communication, propulsion and control as well as manufacturability. This paper sets out the potential applications of such FWNAVs and reviews some of the challenges related to aerodynamics, stability, and design trends.

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Conference papers on the topic "Micro-UAV control"

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Kaya, Derya, Ali Tevfik Büyükkoçak, Ali T. Kutay, and Ozan Tekinalp. "Design and Control of a Micro UAV." In AIAA Atmospheric Flight Mechanics Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-3857.

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Ceccarelli, Nicola, John J. Enright, Emilio Frazzoli, Steven J. Rasmussen, and Corey J. Schumacher. "Micro UAV Path Planning for Reconnaissance in Wind." In 2007 American Control Conference. IEEE, 2007. http://dx.doi.org/10.1109/acc.2007.4282479.

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Ye, Dan, Qiang Wang, and Jie Li. "Fast sliding mode tracking control of micro quadrotor UAV." In 2016 Chinese Control and Decision Conference (CCDC). IEEE, 2016. http://dx.doi.org/10.1109/ccdc.2016.7531062.

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Kojima, Ryota, Kakuji Ogawara, and Hidenori Shingin. "Micro UAV Holonomy Control System Robust for Gust Wind." In 48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2010. http://dx.doi.org/10.2514/6.2010-852.

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Cheng, Fang, Tao Ye, and Sheng Wei. "Control System Design for Silicon MEMS-based Micro UAV." In The Proceedings of the Multiconference on "Computational Engineering in Systems Applications". IEEE, 2006. http://dx.doi.org/10.1109/cesa.2006.313479.

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Cheng, Fang Jian, Tao Ye, and Sheng Wei. "Control System Design for Silicon MEMS-based Micro UAV." In Multiconference on "Computational Engineering in Systems Applications. IEEE, 2006. http://dx.doi.org/10.1109/cesa.2006.4281991.

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Yuan, Zhenhui, Xiwei Huang, Lingling Sun, and Jie Jin. "Software defined mobile sensor network for micro UAV swarm." In 2016 IEEE International Conference on Control and Robotics Engineering (ICCRE). IEEE, 2016. http://dx.doi.org/10.1109/iccre.2016.7476140.

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Voos, Holger. "Nonlinear control of a quadrotor micro-UAV using feedback-linearization." In 2009 IEEE International Conference on Mechatronics. IEEE, 2009. http://dx.doi.org/10.1109/icmech.2009.4957154.

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Bohdanov, Denys, and Hugh Liu. "Vision-based Quadrotor Micro-UAV Position and Yaw Estimation and Control." In AIAA Guidance, Navigation, and Control Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2012. http://dx.doi.org/10.2514/6.2012-5048.

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Li, Shuo, Yi Chai, Maoyun Guo, and Yunling Liu. "Research on Detection Method of UAV Based on micro-Doppler Effect." In 2020 39th Chinese Control Conference (CCC). IEEE, 2020. http://dx.doi.org/10.23919/ccc50068.2020.9189414.

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