This paper presents the estimation method for uncertain parameters in flight vehicles, especially missile systems, based on physics-informed neural networks (PINNs) augmented with a novel integration-based loss. The proposed method identifies four types of structured uncertainty: burnout time, rocket motor tilt angle, location of the center of pressure, and control fin bias, which significantly affect the missile performance. In the estimation framework, as neural networks (NNs) are updated, these uncertainties are also identified simultaneously because they are also included in the structure of NNs. After testing 100 simulation data, the average estimation errors are within 1% of the mean value for each type of uncertainty. The methodology is able to identify the parameters despite noise corruption in the time-series data. Compared with the conventional PINNs, adding the new loss based on the integration of differential equations yields a more reliable estimation performance for all types of uncertainty. This approach can be effective for complex systems and ill-posed inverse problems, which makes it applicable to other aerospace systems.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Identification of Uncertain Parameter in Flight Vehicle Using Physics-Informed Deep Learning


    Contributors:

    Published in:

    Publication date :

    2023-10-25


    Size :

    16 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Physics-informed neural networks for learning dynamic, distributed and uncertain systems

    Praditia, Timothy | HENRY – Federal Waterways Engineering and Research Institute (BAW) | 2023

    Free access



    Physics Informed Deep Learning for Traffic State Estimation

    Huang, Jiheng / Agarwal, Shaurya | IEEE | 2020