This thesis examines the application of artificial neural networks (NNs) to the problem of dynamic flight control. The specific application is the control of a flying model helicopter. The control interface is provided through a hardware and software test bed called the Fast Adaptive Maneuvering Experiment (FAME). The NN design approach uses two NNs: one trained as an emulator of the plant and the other trained to control the emulator. The emulator neural network is designed to reproduce the flight dynamics of the experimental plant. The controller is then designed to produce the appropriate control inputs to drive the emulator to a desired final state. Previous research in the area of NNs for controls has almost exclusively been applied to simulations. To develop a controller for a real plant, a neural network must be created which will accurately recreate the dynamics of the plant. This thesis demonstrates the ability of a neural network to emulate a real, dynamic, nonlinear plant. Neural networks, Helicopter control, Fast Adaptive Maneuvering Experiment (FAME).


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    Title :

    Neural Networks for Dynamic Flight Control


    Contributors:

    Publication date :

    1993


    Size :

    72 pages


    Type of media :

    Report


    Type of material :

    No indication


    Language :

    English






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