This study proposes a comprehensive framework for the identification of nonlinear dynamics in Unmanned Aerial Vehicles (UAVs), integrating data-driven methodologies with theoretical modeling approaches. Two principal techniques are employed: Proportional-Derivative (PD)-based control input approximation and Sparse Identification of Nonlinear Dynamics (SINDy). Addressing the inherent platform constraints—where control inputs are restricted to specific attitude angles and z-axis velocities—thrust and torque are approximated via a PD controller, which serves as a practical intermediary for facilitating nonlinear system identification. Both methodologies leverage data-driven strategies to construct compact and interpretable models from experimental data, capturing significant nonlinearities with high fidelity. The resulting models are rigorously evaluated within a Model Predictive Control (MPC) framework, demonstrating their efficacy in precise trajectory tracking. Furthermore, the integration of data-driven insights enhances the accuracy of the identified models and improves control performance. This framework offers a robust and adaptable solution for analyzing UAV dynamics under realistic operational conditions, emphasizing the comparative strengths and applicability of each modeling approach.


    Access

    Download


    Export, share and cite



    Title :

    SINDy and PD-Based UAV Dynamics Identification for MPC


    Contributors:


    Publication date :

    2025




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Data-Based Spacecraft Rendezvous Control by SINDy

    Tan, Jin / Duan, Guang-Ren / Shi, Mingming et al. | Springer Verlag | 2025


    MPC-SINDy-Based Method on Quadrotor UAV Control with Unknown Environmental Disturbance

    Chen, Maoyang / Zhang, Dianhao / Xu, Yiming | Springer Verlag | 2025


    SINDy analysis of disturbance and plant model superposition on a rolling delta wing

    Le Provost, Mathieu / Williams, David R. / Brunton, Steven | AIAA | 2018


    Load dynamics identification

    Professor Kozlowski, Krzysztof | Springer Verlag | 1998