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Author(s): Elkhidir Tay Allah Yousif Elkhidir, Abdelfattah Hafiz MohammedAhmed.

Volume/Issue: Volume 7, Issue 1 (2026)

Page No: 1-8

Journal: African Journal of Electrical and Electronics Research (AJEER)

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Abstract:

This paper presents the design and implementation of a machine learning-based framework aimed at enhancing the numerical modeling of nonlinear flight dynamics in unmanned aerial vehicles (UAVs). Traditional numerical solvers, though reliable, face challenges in real-time computation and adaptability to nonlinear aerodynamic variations. The proposed hybrid framework integrates data-driven learning components with classical numerical analysis to improve both computational efficiency and modeling precision. Using hybrid datasets from simulations and experimental flights, Long Short-Term Memory (LSTM) and Transformer architectures were developed as numerical surrogates for nonlinear state estimation. The results demonstrate that the proposed machine learning-enhanced framework outperforms the conventional fourth-order Runge–Kutta (RK4) method in terms of accuracy, adaptability, and computation time, achieving up to a tenfold improvement in inference speed. Furthermore, the model shows robustness under disturbances and physical interpretability through feature attribution analyses. This study represents a crucial intermediate phase in developing a comprehensive machine learning–driven numerical environment for nonlinear UAV flight dynamics analysis and control.

Keywords:

UAVs, Nonlinear Flight Dynamics, Machine Learning, Numerical Analysis, Data-Driven Modeling, Hybrid Framework, Real-Time Simulation

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