The purpose of this paper is to present the application of the neural-based estimation method, Neural-Gauss-Newton (NGN), using the real flight data of a small unmanned aerial vehicle (UAV).

    Design/methodology/approach

    The UAVs in general are lighter in weight and their flight is usually influenced by the atmospheric winds because of their relatively lower cruise speeds. During the presence of the atmospheric winds, the aerodynamic forces and moments get modified significantly and the accurate mathematical modelling of the same is highly challenging. This modelling inaccuracy during parameter estimation is routinely treated as the process noise. Furthermore, because of the limited dimensions of the small UAVs, the measurements are usually influenced by the disturbances caused by other subsystems. To handle these measurement and process noises, the estimation methods based on neural networks have been found reliable in the manned aircrafts.

    Findings

    Six sets of compatible longitudinal flight data of the designed UAV have been chosen to estimate the parameters using the NGN method. The consistency in the estimates is verified from the obtained mean and the standard deviation and the same has been validated by the proof-of-match exercise. It is evident from the results that the NGN method was able to perform on a par with the conventional maximum likelihood method.

    Originality/value

    This is a partial outcome of the research carried out in estimating parameters from the UAVs.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Parameter estimation of UAV from flight data using neural network


    Contributors:

    Published in:

    Publication date :

    2018-03-05


    Size :

    10 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English