Determination of fuel mass in aircraft tanks is a nonlinear estimation problem, in which a set of noisy sensor readings is transformed into a single estimate. In the typical commercial aircraft currently in service, this model-based transformation is approximated by a sum of one-dimensional look-up tables representing contributions from individual fuel quantity probes. Significant improvements in accuracy may be achieved by replacing this decomposed approach with a multi-dimensional optimal estimator In the paper feedforward neural nets are trained to approximate the unknown optimal estimator A simulation example demonstrates the benefits offered by the neural net technique compared with the traditional method. Issues related to certification of safety-critical software containing neural net modules are also addressed.


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    Titel :

    Fuel mass estimation in aircraft tanks using neural nets


    Beteiligte:


    Erscheinungsdatum :

    2001


    Format / Umfang :

    6 Seiten, 5 Quellen




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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