This paper deals with the fault detection and estimation scheme for a class of nonlinear systems. An adaptive observer is designed where the unknown nonlinear term can be approximated and the fault is estimated based on radial basis function (RBF) neural network, respectively. The stability of the designed observer is also proved. Finally, simulation results based on an aircraft are presented to evaluate the performance of the proposed observer and the effectiveness of the fault estimation.


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    Titel :

    Fault detection and estimation for a class of nonlinear systems based on neural network observer


    Beteiligte:
    Ruonan Wang (Autor:in) / Bin Jiang (Autor:in) / Jianwei Liu (Autor:in)


    Erscheinungsdatum :

    01.08.2016


    Format / Umfang :

    268001 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch






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