Relevant bibliographies by topics / Quad-rotor helicopters

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Quad-rotor helicopters'

Author: Grafiati
Published: 4 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Quad-rotor helicopters"

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Wang, Jialiang, Hai Zhao, Yuanguo Bi, Shiliang Shao, Qian Liu, Xingchi Chen, Ruofan Zeng, Yu Wang, and Le Ha. "An Improved Fast Flocking Algorithm with Obstacle Avoidance for Multiagent Dynamic Systems." Journal of Applied Mathematics 2014 (2014): 1–13. http://dx.doi.org/10.1155/2014/659805.

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Flocking behavior is a common phenomenon in nature, such as flocks of birds and groups of fish. In order to make the agents effectively avoid obstacles and fast form flocking towards the direction of destination point, this paper proposes a fast multiagent obstacle avoidance (FMOA) algorithm. FMOA is illustrated based on the status of whether the flocking has formed. If flocking has not formed, agents should avoid the obstacles toward the direction of target. If otherwise, these agents have reached the state of lattice and then these agents only need to avoid the obstacles and ignore the direction of target. The experimental results show that the proposed FMOA algorithm has better performance in terms of flocking path length. Furthermore, the proposed FMOA algorithm is applied to the formation flying of quad-rotor helicopters. Compared with other technologies to perform the localization of quad-rotor helicopter, this paper innovatively constructs a smart environment by deploying some wireless sensor network (WSN) nodes using the proposed localization algorithm. Finally, the proposed FMOA algorithm is used to conduct the formation flying of these quad-rotor helicopters in the smart environment.
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Okyere, Emmanuel, Amar Bousbaine, Gwangtim T. Poyi, Ajay K. Joseph, and Jose M. Andrade. "LQR controller design for quad-rotor helicopters." Journal of Engineering 2019, no. 17 (June 1, 2019): 4003–7. http://dx.doi.org/10.1049/joe.2018.8126.

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Izadi, Hojjat A., Youmin Zhang, and Brandon W. Gordon. "Fault Tolerant Model Predictive Control of Quad-Rotor Helicopters with Actuator Fault Estimation." IFAC Proceedings Volumes 44, no. 1 (January 2011): 6343–48. http://dx.doi.org/10.3182/20110828-6-it-1002.03709.

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MORI, Keita, Katsuya HOTTA, and Manabu YAMADA. "Adaptive H^|^infin; Tracking Control for Quad-rotor Helicopters Based on Input-output Linearization." Transactions of the Society of Instrument and Control Engineers 50, no. 11 (2014): 784–91. http://dx.doi.org/10.9746/sicetr.50.784.

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Bautista-Medina, José Antonio, Rogelio Lozano, and Antonio Osorio-Cordero. "Modeling and Control of a Single Rotor Composed of Two Fixed Wing Airplanes." Drones 5, no. 3 (September 8, 2021): 92. http://dx.doi.org/10.3390/drones5030092.

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This paper proposes a simple flying rotor prototype composed of two small airplanes attached to each other with a rigid rod so that they can rotate around themselves. The prototype is intended to perform hover flights with more autonomy than existing classic helicopters or quad-rotors. Given that the two airplanes can fly apart from each other, the induced flow which normally appears in rotorcrafts will be significantly reduced. The issue that is addressed in the paper is how this flying rotor prototype can be modeled and controlled. A model of the prototype is obtained by computing the kinetic and potential energies and applying the Euler Lagrange equations. Furthermore, in order to simplify the equations, it has been considered that the yaw angular displacement evolves much faster than the other variables. Furthermore a study is presented to virtually create a swashplate which is a central mechanism in helicopters. Such virtual swashplate is created by introducing a sinusoidal control on the airplanes’ elevators. The torque amplitude will be proportional to the sinusoidal amplitude and the direction will be determined by the phase of the sinusoidal. A simple nonlinear control algorithm is proposed and its performance is tested in numerical simulations.
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Penkov, I., and D. Aleksandrov. "Analysis and study of the influence of the geometrical parameters of mini unmanned quad-rotor helicopters to optimise energy saving." INTERNATIONAL JOURNAL OF AUTOMOTIVE AND MECHANICAL ENGINEERING 14, no. 4 (December 30, 2017): 4730–46. http://dx.doi.org/10.15282/ijame.14.4.2017.11.0372.

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Hayakawa, Hayami, Shogo Obata, and Manabu Yamada. "Tracking control of quad-rotor helicopters suspended a power supply cable with online estimation of disturbances based on input-output linearisation." International Journal of Advanced Mechatronic Systems 7, no. 2 (2016): 114. http://dx.doi.org/10.1504/ijamechs.2016.082629.

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Yamada, Manabu, Shogo Obata, and Hayami Hayakawa. "Tracking control of quad-rotor helicopters suspended a power supply cable with online estimation of disturbances based on input-output linearisation." International Journal of Advanced Mechatronic Systems 7, no. 2 (2016): 114. http://dx.doi.org/10.1504/ijamechs.2016.10003629.

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Wang, Jialiang, Jianli Ding, Weidong Cao, Quanfu Li, and Hai Zhao. "Neural network fuzzy control for enhancing the stability performance of quad-rotor helicopter." Transactions of the Institute of Measurement and Control 40, no. 11 (December 14, 2017): 3333–44. http://dx.doi.org/10.1177/0142331217713837.

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Recently, the quad-rotor helicopter has gained increasing attention owing to its very good flexibility, its ability to execute various flight missions even in harsh environments. The quad-rotor helicopter can implement different fight attitudes, which is attributed to the effective control of the motor speed about four propellers. In order to make the quad-rotor helicopter can better finish flight mission, the performance of flight stability then becomes particularly important. A neural network fuzzy control algorithm is proposed in this paper so as to guarantee the stability performance of the quad-rotor helicopter. The proposed algorithm is based on the neural network, which keeps the self-organization and self-learning ability, besides this, it utilizes the strong impression ability of constitutive knowledge as to the fuzzy logic. The proposed control scheme aims to implement good abilities such as describing qualitative knowledge, strong learning mechanism and direct processing about quantitative data of the quad-rotor helicopter. In the practical flight process of the quad-rotor helicopter, while the deviation of position and attitude information become larger, fuzzy control is adopted so as to shorten the overshoot and adjustment time. On the other hand, if the deviation of position and attitude become relatively smaller, neural network PID control will be used so as to reduce the error. Experimental results show that the proposed neural network fuzzy control algorithm exhibits good performance in the flight process of the quad-rotor helicopter.
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Wang, Jialiang, Hai Zhao, Yuanguo Bi, Xingchi Chen, Ruofan Zeng, and Shiliang Shao. "Quad-Rotor Helicopter Autonomous Navigation Based on Vanishing Point Algorithm." Journal of Applied Mathematics 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/567057.

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Quad-rotor helicopter is becoming popular increasingly as they can well implement many flight missions in more challenging environments, with lower risk of damaging itself and its surroundings. They are employed in many applications, from military operations to civilian tasks. Quad-rotor helicopter autonomous navigation based on the vanishing point fast estimation (VPFE) algorithm using clustering principle is implemented in this paper. For images collected by the camera of quad-rotor helicopter, the system executes the process of preprocessing of image, deleting noise interference, edge extracting using Canny operator, and extracting straight lines by randomized hough transformation (RHT) method. Then system obtains the position of vanishing point and regards it as destination point and finally controls the autonomous navigation of the quad-rotor helicopter by continuous modification according to the calculated navigation error. The experimental results show that the quad-rotor helicopter can implement the destination navigation well in the indoor environment.
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Dissertations / Theses on the topic "Quad-rotor helicopters"

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Poyi, Gwangtim Timothy. "A novel approach to the control of quad-rotor helicopters using fuzzy-neural networks." Thesis, University of Derby, 2014. http://hdl.handle.net/10545/337911.

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Quad-rotor helicopters are agile aircraft which are lifted and propelled by four rotors. Unlike traditional helicopters, they do not require a tail-rotor to control yaw, but can use four smaller fixed-pitch rotors. However, without an intelligent control system it is very difficult for a human to successfully fly and manoeuvre such a vehicle. Thus, most of recent research has focused on small unmanned aerial vehicles, such that advanced embedded control systems could be developed to control these aircrafts. Vehicles of this nature are very useful when it comes to situations that require unmanned operations, for instance performing tasks in dangerous and/or inaccessible environments that could put human lives at risk. This research demonstrates a consistent way of developing a robust adaptive controller for quad-rotor helicopters, using fuzzy-neural networks; creating an intelligent system that is able to monitor and control the non-linear multi-variable flying states of the quad-rotor, enabling it to adapt to the changing environmental situations and learn from past missions. Firstly, an analytical dynamic model of the quad-rotor helicopter was developed and simulated using Matlab/Simulink software, where the behaviour of the quad-rotor helicopter was assessed due to voltage excitation. Secondly, a 3-D model with the same parameter values as that of the analytical dynamic model was developed using Solidworks software. Computational Fluid Dynamics (CFD) was then used to simulate and analyse the effects of the external disturbance on the control and performance of the quad-rotor helicopter. Verification and validation of the two models were carried out by comparing the simulation results with real flight experiment results. The need for more reliable and accurate simulation data led to the development of a neural network error compensation system, which was embedded in the simulation system to correct the minor discrepancies found between the simulation and experiment results. Data obtained from the simulations were then used to train a fuzzy-neural system, made up of a hierarchy of controllers to control the attitude and position of the quad-rotor helicopter. The success of the project was measured against the quad-rotor’s ability to adapt to wind speeds of different magnitudes and directions by re-arranging the speeds of the rotors to compensate for any disturbance. From the simulation results, the fuzzy-neural controller is sufficient to achieve attitude and position control of the quad-rotor helicopter in different weather conditions, paving way for future real time applications.
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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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Kemp, Christopher. "Visual control of a miniature quad-rotor helicopter." Thesis, University of Cambridge, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.614161.

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Gupta, Vinit. "Quad tilt rotor simulations in helicopter mode using computational fluid dynamics." College Park, Md. : University of Maryland, 2005. http://hdl.handle.net/1903/3172.

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Thesis (Ph. D.) -- University of Maryland, College Park, 2005.
Thesis research directed by: Aerospace Engineering. Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.
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Book chapters on the topic "Quad-rotor helicopters"

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Diveev, Askhat, Oubai Hussein, Elizaveta Shmalko, and Elena Sofronova. "Synthesis of Control System for Quad-Rotor Helicopter by the Network Operator Method." In Advances in Intelligent Systems and Computing, 246–63. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55180-3_20.

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An, Jongwoo, and Jangmyung Lee. "Using IMU Sensor and EKF Algorithm in Attitude Control of a Quad-Rotor Helicopter." In Intelligent Autonomous Systems 15, 933–42. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01370-7_72.

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Abdolhosseini, Mahyar, Youmin M. Zhang, and Camille Alain Rabbath. "Trajectory Tracking with Model Predictive Control for an Unmanned Quad-rotor Helicopter: Theory and Flight Test Results." In Intelligent Robotics and Applications, 411–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33509-9_41.

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Conference papers on the topic "Quad-rotor helicopters"

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Pilz, Ulf, Andrey P. Popov, and Herbert Werner. "Robust controller design for formation flight of quad-rotor helicopters." In 2009 Joint 48th IEEE Conference on Decision and Control (CDC) and 28th Chinese Control Conference (CCC). IEEE, 2009. http://dx.doi.org/10.1109/cdc.2009.5400593.

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Hayakawa, Hayami, Shogo Obata, and Manabu Yamada. "Tracking control of quad-rotor helicopters suspended a power supply cable with on-line estimation of disturbances." In 2015 International Conference on Advanced Mechatronic Systems (ICAMechS). IEEE, 2015. http://dx.doi.org/10.1109/icamechs.2015.7287148.

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Pandey, Anshuman, Mark L. Sutkowy, Matthew McCrink, and James W. Gregory. "Aerodynamic Characterization of a Quad-Rotor Helicopter." In 2018 AIAA Aerospace Sciences Meeting. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2018. http://dx.doi.org/10.2514/6.2018-1526.

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Bestaoui, Yasmina, and Rajia Slim. "Maneuvers for a Quad-Rotor Autonomous Helicopter." In AIAA Infotech@Aerospace 2007 Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2007. http://dx.doi.org/10.2514/6.2007-2728.

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Bousbaine, A., M. H. Wu, and G. T. Poyi. "Modelling and simulation of a quad-rotor helicopter." In 6th IET International Conference on Power Electronics, Machines and Drives (PEMD 2012). IET, 2012. http://dx.doi.org/10.1049/cp.2012.0318.

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Mkrtchyan, Armen, Richard Schultz, and William Semke. "Vision-Based Autopilot Implementation Using a Quad-Rotor Helicopter." In AIAA Infotech@Aerospace Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2009. http://dx.doi.org/10.2514/6.2009-1831.

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Qiao, Jing, Zhixiang Liu, and Youmin Zhang. "Gain scheduling PID control of the quad-rotor helicopter." In 2017 IEEE International Conference on Unmanned Systems (ICUS). IEEE, 2017. http://dx.doi.org/10.1109/icus.2017.8278414.

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Hossain, M. Raju, D. Geoff Rideout, and D. Nicholas Krouglicof. "Bond graph dynamic modeling and stabilization of a quad-rotor helicopter." In the 2010 Spring Simulation Multiconference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1878537.1878761.

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Jin, Huasu, Xiaoli Li, Xiaodong Zhang, and Yang Li. "Study on the modeling methods of the quad-rotor unmanned helicopter." In 2015 27th Chinese Control and Decision Conference (CCDC). IEEE, 2015. http://dx.doi.org/10.1109/ccdc.2015.7162781.

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Serirojanakul, Apichart, and Manop Wongsaisuwan. "Optimal control of quad-rotor helicopter using state feedback LPV method." In 2012 9th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON 2012). IEEE, 2012. http://dx.doi.org/10.1109/ecticon.2012.6254219.

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