Skip to main content

Adaptive Neural Control-Oriented Models of Unmanned Aerial Vehicles

  • 826 Accesses

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 368)

Abstract

From real input/output data, different models of an unmanned aerial vehicle are obtained by applying adaptive neural networks. These models are control-oriented; their main objective is to help us to design, implement and simulate different intelligent controllers and to test them on real systems. The influence of the selected training data on the final model is also discussed. They have been compared to off-line learning neural models with satisfactory results in terms of accuracy and computational cost.

Keywords

  • Adaptive neural networks
  • Soft computing
  • Modeling
  • Identification
  • Unmanned aerial vehicles (UAV)

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-19719-7_29
  • Chapter length: 9 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   169.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-19719-7
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   219.99
Price excludes VAT (USA)
Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. Billings SA (2013) Non-linear system identification: NARMAX methods in the time, frequency, and spatio-temporal domains. Wiley

    Google Scholar 

  2. Deng J (2013) Dynamic neural networks with hybrid structures for nonlinear system identification. Eng Appl Artif Intell 26(1):281–292

    CrossRef  Google Scholar 

  3. Han HG, Qiao JF (2012) Adaptive computation algorithm for RBF neural network. IEEE Trans Neural Netw Learn Syst 23:2

    CrossRef  Google Scholar 

  4. Hoffer NV, Coopmans C, Jensen AM, Chen Y (2014) A survey and categorization of small low-cost unmanned aerial vehicle system identification. J Intell Rob Syst 74(1–2):129–145

    CrossRef  Google Scholar 

  5. Nemes A (2015) Synopsis of soft computing techniques used in quadrotor UAV modelling and control. Interdiscipl Descr Complex Syst 13(1):15–25

    CrossRef  Google Scholar 

  6. San Martin R, Barrientos A, Gutierrez P, del Cerro J (2006) Unmanned aerial vehicle (UAV) modelling based on supervised neural networks. In: Proceedings of the international conf robotics and automation, IEEE, pp 2497–2502

    Google Scholar 

  7. Santos M, López R, de la Cruz JM (2006) A neuro-fuzzy approach to fast ferry vertical motion modelling. Eng Appl Artif Intell 19:313–321

    CrossRef  Google Scholar 

  8. Santos M (2011) Un enfoque aplicado del control inteligente. RIAI 8(4):283–296

    CrossRef  Google Scholar 

  9. Sierra JE, Santos M (2013) Estudio comparativo de modelos de un vehículo aéreo obtenidos mediante técnicas analíticas y basadas en redes neuronales. In: CEDI, pp 1270–1279

    Google Scholar 

  10. Parrot. http://www.parrot.com

  11. Control Engineering Group, Spanish Committee of Automatic (CEA). http://www.ceautomatica.es/og/ingenieria-de-control

  12. Sayama H, Pestov I, Schmidt J, Bush BJ, Wong C, Yamanoi J, Gross T (2013) Modeling complex systems with adaptive networks. Comput Math Appl 65(10):1645–1664

    CrossRef  MathSciNet  Google Scholar 

  13. Ugalde HMR, Carmona JC, Alvarado VM, Reyes-Reyes J (2013) Neural network design and model reduction approach for black box nonlinear system identification with reduced number of parameters. Neurocomputing 101:170–180

    CrossRef  Google Scholar 

Download references

Acknowledgments

The authors would like to acknowledge the data provided by the Control Engineering Group of the Spanish Committee of Automatic [10]. Author Matilde Santos would like to thank the Spanish Ministry of Science and Innovation (MICINN) for support under project DPI2013-46665-C2-1-R. Authors would also like to thank the reviewers for their useful comments.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to J. Enrique Sierra .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Enrique Sierra, J., Santos, M. (2015). Adaptive Neural Control-Oriented Models of Unmanned Aerial Vehicles. In: Herrero, Á., Sedano, J., Baruque, B., Quintián, H., Corchado, E. (eds) 10th International Conference on Soft Computing Models in Industrial and Environmental Applications. Advances in Intelligent Systems and Computing, vol 368. Springer, Cham. https://doi.org/10.1007/978-3-319-19719-7_29

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-19719-7_29

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19718-0

  • Online ISBN: 978-3-319-19719-7

  • eBook Packages: EngineeringEngineering (R0)