The dynamic nature of quadrotor flight introduces significant uncertainty in system parameters, such as thrust and drag factors. Consequently, operators grapple with escalating challenges in implementing real-time control actions. This study presents an approach for estimating the dynamic model of Unmanned Aerial Vehicles based on Physics-Informed Neural Networks (PINNs), which is of paramount importance due to the presence of uncertain data and since control actions are required in very short computation times. In this regard, by including physical laws into neural networks, PINNs offer the potential to tackle several issues, such as heightened non-linearities in low-inertia systems, elevated measurement noise, and constraints on data availability or uncertainties, while ensuring the robustness of the solution, thus ensuring effective results in short time, once the network training has been performed and without the need to be retrained. The effectiveness of the proposed method is showcased in a simulation environment with real data and juxtaposed with a state-of-the-art technique, such as the Extended Kalman Filter (EKF). The results show that the proposed estimator outperforms the EKF both in terms of the efficacy of the solution and computation time.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Physics-Informed Neural Networks for Unmanned Aerial Vehicle System Estimation


    Beteiligte:


    Erscheinungsdatum :

    2024




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Unbekannt




    Physics-Informed Neural Networks for Satellite State Estimation

    Varey, Jacob / Ruprecht, Jessica D. / Tierney, Michael et al. | IEEE | 2024


    Unmanned aerial vehicle and unmanned aerial vehicle system

    WANG JIEKAI / ZHOU GUANGHAI / LIU SHAOYI et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    UNMANNED AERIAL VEHICLE AND UNMANNED AERIAL VEHICLE SYSTEM

    KWAK PILWON / KIM DAEUN / SEO JEONGKYO | Europäisches Patentamt | 2021

    Freier Zugriff

    Unmanned aerial vehicle and Unmanned aerial vehicle system

    PILWON KWAK / DA EUN KIM / JEONGKYO SEO | Europäisches Patentamt | 2021

    Freier Zugriff