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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    Title :

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


    Contributors:
    Ruonan Wang (author) / Bin Jiang (author) / Jianwei Liu (author)


    Publication date :

    2016-08-01


    Size :

    268001 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English






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