Aggressive Manuevering of Unmanned Helicopters: Learning from Human Based on Neural Networks

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 193)

Abstract

“Teaching by Showing” control of a small helicopter’s aggressive maneuvering often needs inner aided controllers based on helicopter’s dynamics, which is very complex to identify. In this paper, a neural network based control is proposed, based on the identification of the relationship between the pilot’s control and flight states, and it is a model-free control method. Flight test is done in simulation environment based on real flight data. The results show the effectiveness of the neural network based controller for aggressive flight control.

Keywords

neural network unmanned helicopter learning flight test 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Montgomery, J.F., Bekey, G.A.: Learning helicopter control through teaching by showing. In: Proceedings of the 37th IEEE Conference on Decision and Control, vol. 4 (1998)Google Scholar
  2. 2.
    Wyeth, G., Wyeth, G., Roberts, J.: Autonomous helicopter hover using an artificial neural network. In: Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation, vol. 2, pp. 1635–1640 (2001)Google Scholar
  3. 3.
    Bagnell, J.A., Schneider, J.G.: Autonomous helicopter control using reinforcement learning policy search methods. In: Proceedings of the 2001 IEEE International Conference on Robotics and Automation, Seoul, Korea (May 2001)Google Scholar
  4. 4.
    Ng, A.Y., Kim, H.J., Jordan, M.I., Sastry, S.: Autonomous helicopter flight via reinforcement learning. In: Advances in Neural Information Processing Systems, vol. 16 (2004)Google Scholar
  5. 5.
    Ng, A.Y., Coates, A., Diel, M., Ganapathi, V., Schulte, J., Tse, B., Berger, E., Liang, E.: Autonomous inverted helicopter flight via reinforcement learning. In: International Symposium on Experimental Robotics (2004)Google Scholar
  6. 6.
    Calise, A.J., Kim, B.S., Leitner, J., Prasad, J.V.R.: Helicopter adaptive flight control using neural networks. In: Proceedings of the 33rd IEEE Conference on Decision and Control, vol. 4 (1994)Google Scholar
  7. 7.
    Johnson, E.N., Kannan, S.K.: Adaptive trajectory control for autonomous helicopters. Journal of Guidance, Control and Dynamics 28(3) (2005)Google Scholar
  8. 8.
    Abbeel, P., Coates, A., Ng, A.Y.: Autonomous helicopter aerobatics through apprenticeship learning. The International Journal of Robotics Research (2010)Google Scholar
  9. 9.
    Gavrilets, V.: Autonomous aerobatic maneuvering of miniature helicopters. Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics (2003)Google Scholar
  10. 10.
    Rumelhart, D.E., Hintont, G.E., Williams, R.J.: Learning representations by back-propagating errors. Nature 323(6088), 533–536 (1986)CrossRefGoogle Scholar
  11. 11.
    Qi, J., Song, D., Dai, L., Han, J., Wang, Y.: The New Evolution for SIA Rotorcraft UAV Project. Journal of Robotics 1(9) (2010)Google Scholar
  12. 12.
    Olson, C.L.: FlightGear Flight Simulator (2010), http://www.flightgear.org/Downloads/aircraft

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.State Key Laboratory of RoboticsShenyang Institute of Automation, Chinese Academy of SciencesShenyangChina

Personalised recommendations