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.
