A robust neuro-control scheme is presented for aircraft auto-landing under severe wind conditions and partial loss of control surfaces. In the scheme, a dynamic radial basis function network (RBFN) called minimal resource allocating network (MRAN), that incorporates a growing and pruning strategy, is utilize to aid an H/sub /spl infin// controller using a feedback-error-learning mechanism. The neural network uses only online learning and is not trained "a priori". Specifically, the performance of this neuro-controller for aircraft auto-landing in a microburst along with a partial loss of control effectiveness is analyzed and compared with other control schemes. Simulation studies show that the performance obtained by the neuro-H/sub /spl infin// control scheme is better than the other control schemes under failure and extreme wind conditions.


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    Titel :

    Robust neuro-H/sub /spl infin// controller design for aircraft auto-landing


    Beteiligte:
    Yan Li, (Autor:in) / Sundararajan, N. (Autor:in) / Saratchandran, P. (Autor:in) / Zhifeng Wang, (Autor:in)


    Erscheinungsdatum :

    01.01.2004


    Format / Umfang :

    1088336 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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