Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Rule Induction of Automotive Historic Styles Using Decision Tree Classifier Read chapter Read first chapter

2020 | OriginalPaper | Chapter

Design a Neural Controller to Control Rescue Quadcopter in Hang Status

Authors : Nguyen Hoang Mai, Le Quoc Huy, Tran The Son

Published in: Advances in Computational Collective Intelligence

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Quadcopters can be used for various applications in many fields including service, life, security, military… This is a research of oriented application development prospects in the near future because of the quadcopters ability to move flexibly regardless of the terrain and can support human issues that quadcopter on the ground do not. However, due to the motion characteristics in the air, so the quadcopter has certain limitations, including moving the static problem, the suspend state of the quadcopter. This is a complex subject and many scientists are interested in studying the development of large size quadcopters that can carry both people and heavy equipment. This paper analyzes some problems associated with quadcopter motion in a static state and designs a neural controller as the basis for developing more advanced applications in practice to manufacture the big quadcopter for load. The simulation results illustrate the problem and explain the relevance of the theory.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Melanoma Skin Cancer Classification Using Transfer Learning
next chapter Multidimensional Analysis of SCADA Stream Data for Estimating the Energy Efficiency of Mining Transport
Literature
1.
Jafari, M., Xu, H.: Intelligent control for unmanned aerial systems with system uncertain and disturbances using artificial neural network. In: MDPI2018, vol. 89557, University of Nevada, Reno (2018)
2.
Amer, K., Samy, M., ElHakim, R., Shaker, M., ElHelw, M.: Convolutional neural network-based deep urban signatures with application to drone localization. In: IEEE International Conference on Computer Vision Workshops (ICCVW), pp. 2138–2145. IEEE (2017)
3.
Amer, K., Samy, M., Shaker, M., ElHelw, M.: Deep convolutional neural network-based autonomous drone navigation, arXiv:​1801.​08214,Center for Informatics Science Nile University Giza, Egypt (2018)
4.
Padhy, R.P., Verma, S., Ahmad, S., Choudhury, S.K.: Deep neural network for autonomous UAV navigation in indoor corridor environments. In: International Conference on Robotics and Smart Manufacturing (RoSMa2018), pp. 643–650, Elsevier (2018)
5.
De Mel, D.H., Stol, K.A., Mills, J.A., Eastwood, B.R.: Vision-based object path following on a quadcopter for GPS-denied environments. In: IEEE International Conference on Unmanned Aircraft Systems (ICUAS), pp. 456–461 (2017)
6.
Johnson, W.: Calculated hovering helicopter flight dynamics with a circulation-controlled rotor, Ames research center NASA (1977)
7.
Ribera, M., Celi, R.: Helicopter Flight Dynamics Simulation, Doctor of Philosophy, Department of Aerospace Engineering-University of Maryland (2007)
8.
Houghton, Aerodynamics for engineering students. 5th edn. Buttrworth-Heinemann, England (2003). ISBN 0750651113
9.
Caldera, H.S.M.M., Anuradha, B.W.S.: A self-balancing quadcopter design with autonomous. In: SAITM Research Symposium on Engineering Advancements 2014 (SAITM – RSEA 2014) (2014)
10.
Luukkonen, T.: Modelling and control of quadcopter, Aalto University School of Science, Mat-2.4108, Independent Research Project In Applied Mathematics Espoo, 22 August 2011
11.
Amin, M.Y.: Modeling and neural control for quadcopter helicopter. Yanbu J. Eng. Sci. (2011). ISSN 1658-5321
12.
Teng, Y.-F., Hu, B., Liu, Z-W., Huang, J., Guan, Z.-H.: Adaptive neural network control for quadrotor unmanned aerial vehicles. In: The 11th Asian Control Conference (ASCC), pp. 988–992, Australia (2017)
13.
Shirzadeh, M., Asl, H.J., Amirkhani, A., Jalali, A.A.: Vision-based control of a quadrotor utilizing artificial neural networks for tracking of moving targets. J. Eng. Appl. Artif. Intell. 58, 34–48 (2017)CrossRef
14.
Lower, M., Tarnawski, W.: Quadrotor navigation using the PID and neural network controller. In: Zamojski, W., Mazurkiewicz, J., Sugier, J., Walkowiak, T., Kacprzyk, J. (eds.) Theory and Engineering of Complex Systems and Dependability. AISC, vol. 365, pp. 265–274. Springer, Cham (2015). https://​doi.​org/​10.​1007/​978-3-319-19216-1_​25CrossRef
15.
Padhy, R.P., Verma, S., Ahmad, S., Choudhury, S.K.: Deep neural network for autonomous UAV navigation in indoor corridor environments. In: International Conference on Robotics and Smart Manufacturing (RoSMa2018), Procedia Computer Science, pp. 643–650. Elsevier (2018)
16.
Luo, C., Du, Z., Yu, L.: Neural network control design for an unmanned aerial vehicle with a suspended payload. Electronics 8(9), 931 (2019)CrossRef
17.
Muliadi, J., Kusumoputro, B.: Neural network control system of UAV altitude dynamics and its comparison with the PID control system. Hindawi J. Adv. Transp. Article ID 3823201 (2018)
18.
Celen, B., Oniz, Y.: Trajectory tracking of a quadcopter using fuzzy logic and neural network controllers. In: The 6th IEEE International Conference on Control Engineering and Information Technology (CEIT), Istanbul (2018)
19.
Dierks, T., Jagannathan, S.: Neural network control of quadrotor UAV formations. In: American Control Conference, Hyatt Regency Riverfront, pp. 2990–2996, St. Louis (2009)
20.
Martin, R.S., Barrientos, A., Gutierrez, P., del Cerro, J.: Unmanned Aerial Vehicle (UAV) modelling based on supervised neural networks. In: Proceedings of the 2006 IEEE International Conference on Robotics and Automation, pp. 2497–2502, Orlando (2006)
Metadata
Title
Design a Neural Controller to Control Rescue Quadcopter in Hang Status
Authors
Nguyen Hoang Mai
Le Quoc Huy
Tran The Son
Copyright Year
2020
Book
Advances in Computational Collective Intelligence

Print ISBN: 978-3-030-63118-5

Electronic ISBN: 978-3-030-63119-2

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-63119-2_25

Premium Partner

    Image Credits