This chapter outlines a methodology for computation of aerodynamic derivatives directly from flight data using radial basis function network (RBFN). The delta method using RBFN is first applied to simulated data with added measurement and state noise. The delta method is an artificial neural network (ANN)‐based approach for parameter estimation. The derivative calculated using this method considers all the data points, thus smoothing noisy data. All presented results are obtained using the RBFN‐based delta method. The chapter discusses rotorcraft parameter estimation based on the RBFN‐based delta method. This technique is model‐free and is suitable for rotorcraft unmanned air vehicles (UAVs) and micro air vehicles (MAVs). The method is first evaluated on simulated data generated by a nonlinear simulation model. The helicopter can be mathematically modeled by considering it as a number of subsystems: the main rotor, fuselage, powerplant, empennage, tail rotor and flight control systems. The helicopter dynamics are assessed about its center of gravity.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Aerodynamic Derivative Calculation Using Radial Basis Function Neural Networks


    Beteiligte:
    Marqués, Pascual (Herausgeber:in) / Da Ronch, Andrea (Herausgeber:in) / Ganguli, Ranjan (Autor:in)


    Erscheinungsdatum :

    03.04.2017


    Format / Umfang :

    25 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Unsteady aerodynamic modeling based on fuzzy scalar radial basis function neural networks

    Wang, Xu / Kou, Jiaqing / Zhang, Weiwei | SAGE Publications | 2019



    Vehicle tracking using radial basis function neural networks

    Bullock,D. / Lousiana State Univ.,US | Kraftfahrwesen | 1996


    Vehicle Tracking using Radial Basis Function Neural Networks

    Bullock, D. | British Library Conference Proceedings | 1996


    SSME Sensor Modeling Using Radial Basis Function Neural Networks

    Wheeler, K. R. / Dhawan, A. P. / Meyer, C. M. et al. | British Library Conference Proceedings | 1994