This paper presents an off-line (finite time interval) and on-line learning direct adaptive neural controller for an unstable helicopter. The neural controller is designed to track pitch rate command signal generated using the reference model. A helicopter having a soft inplane four-bladed hingeless main rotor and a four-bladed tail rotor with conventional mechanical controls is used for the simulation studies. For the simulation study, a linearized helicopter model at different straight and level flight conditions is considered. A neural network with a linear filter architecture trained using backpropagation through time is used to approximate the control law. The controller network parameters are adapted using updated rules Lyapunov synthesis. The off-line trained (for finite time interval) network provides the necessary stability and tracking performance. The on-line learning is used to adapt the network under varying flight conditions. The on-line learning ability is demonstrated through parameter uncertainties. The performance of the proposed direct adaptive neural controller (DANC) is compared with feedback error learning neural controller (FENC).


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    Title :

    A direct adaptive neural command controller design for an unstable helicopter


    Additional title:

    Entwurf eines direkten neuronalen Adaptivreglers für einen instabilen Hubschrauber


    Contributors:


    Publication date :

    2009


    Size :

    11 Seiten, 7 Bilder, 1 Tabelle, 33 Quellen




    Type of media :

    Article (Journal)


    Type of material :

    Print


    Language :

    English




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