Autonomous flight of an aerial vehicle requires a sufficiently accurate mathematical model, which can capture system dynamics in the presence of external disturbances. Artificial neural network is known for ideal in capturing systems behaviour, where little knowledge about vehicle dynamics is available. In this paper, we explored this potential of artificial neural network for characterizing nonlinear dynamics of an unmanned airship. The flight experimentation data for an outdoor experimental airship are acquired through a series of pre-determined flight tests. The experimental data are subjected to a class of dynamic recurrent neural network model dubbed as nonlinear auto-regressive model with exogenous inputs for training. Sufficiently trained neural network model captured and demonstrated the longitudinal dynamics of the airship satisfactorily. We also demonstrated the usefulness of proposed technique for Lotte airship, wherein the performance of proposed model is validated and analysed for the Lotte airship flight test data.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Nonlinear auto-regressive neural network for mathematical modelling of an airship using experimental data


    Beteiligte:


    Erscheinungsdatum :

    01.06.2019


    Format / Umfang :

    21 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Comparison of alternative approaches to auto-regressive modelling of dynamic systems

    Markert, Heiner / Schiepe, Christian / Streichert, Felix et al. | Tema Archiv | 2011



    Nonlinear airship aeroelasticity

    Bessert, N. / Frederich, O. | Tema Archiv | 2005


    Bus travel time prediction: a log-normal auto-regressive (AR) modelling approach

    Dhivya Bharathi, B. / Anil Kumar, B. / Achar, Avinash et al. | Taylor & Francis Verlag | 2020