Identification of an unmanned helicopter using neural network

  • Authors:

  • Mahendra Kumar Samal;Sreenatha Anavatti;Matthew Garratt

  • Affiliations:

  • University of New South Wales (UNSW@ADFA), Canberra, Australia;University of New South Wales (UNSW@ADFA), Canberra, Australia;University of New South Wales (UNSW@ADFA), Canberra, Australia

  • Venue:
  • MIC '08 Proceedings of the 27th IASTED International Conference on Modelling, Identification and Control
  • Year:
  • 2008

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Abstract

Black-box identification using neural network applied for modeling the dynamics of the R-Max helicopter is discussed in this paper. The identification is carried out both for coupled six-degree-of-freedom system as well as decoupled longitudinal and lateral dynamics using the test flight data. The simulation results and the statistical error analysis is provided for both the cases. The results indicate the suitability of the black-box method for identification of the nonlinear coupled dynamics for the helicopter. The identified unmanned helicopter model can further be used for design of Automatic Flight Control Systems (AFCS).