• DocumentCode
    857402
  • Title

    Modeling and inverse controller design for an unmanned aerial vehicle based on the self-organizing map

  • Author

    Cho, Jeongho ; Principe, Jose C. ; Erdogmus, Deniz ; Motter, Mark A.

  • Author_Institution
    Comput. NeuroEng. Lab., Univ. of Florida, Gainesville, FL, USA
  • Volume
    17
  • Issue
    2
  • fYear
    2006
  • fDate
    3/1/2006 12:00:00 AM
  • Firstpage
    445
  • Lastpage
    460
  • Abstract
    The next generation of aircraft will have dynamics that vary considerably over the operating regime. A single controller will have difficulty to meet the design specifications. In this paper, a self-organizing map (SOM)-based local linear modeling scheme of an unmanned aerial vehicle (UAV) is developed to design a set of inverse controllers. The SOM selects the operating regime depending only on the embedded output space information and avoids normalization of the input data. Each local linear model is associated with a linear controller, which is easy to design. Switching of the controllers is done synchronously with the active local linear model that tracks the different operating conditions. The proposed multiple modeling and control strategy has been successfully tested in a simulator that models the LoFLYTE UAV.
  • Keywords
    aircraft control; control system synthesis; linear systems; nonlinear control systems; remotely operated vehicles; self-organising feature maps; LoFLYTE UAV; inverse controller design; linear controller; local linear modelling scheme; self-organizing map; unmanned aerial vehicle; Aerospace control; Aircraft; Control systems; Inverse problems; NASA; Nonlinear control systems; Nonlinear dynamical systems; Testing; Unmanned aerial vehicles; Vehicle dynamics; Inverse controller; local linear model; multiple models; self-organizing map (SOM); Aircraft; Algorithms; Computer Simulation; Equipment Design; Equipment Failure Analysis; Feedback; Models, Theoretical; Motion; Neural Networks (Computer); Robotics; Transducers;
  • fLanguage
    English
  • Journal_Title
    Neural Networks, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    1045-9227
  • Type

    jour

  • DOI
    10.1109/TNN.2005.863422
  • Filename
    1603629