V. SANT’ANA; R. LARSON; P. KRUS; R.M. FINZI NETO

DOI Number: 10.13009/EUCASS2023-094

Accurate aerodynamic modeling is crucial for understanding and optimizing the behavior of aircraft systems. This work presents a novel approach to developing a low-cost yet high-fidelity aerodynamic model using machine learning techniques and experimental flight data from a reduced-scale Generic Future Fighter (GFF). The proposed model leverages Neuro-Fuzzy combined with Differential Evolution for training the acquired data, while employing a Fuzzy Rule-Based System (FRBS) with Gaussian-shaped membership functions for inputs. By effectively predicting forces and moments based on input variable values, the developed model serves as a tool for system identification specific to the aircraft under investigation. The results shows that the Neuro-Fuzzy has a good adaptability to this kind of identifications.

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In Categories: 1. Aerodynamics and Flight Physics, 1. Aerospace Europe Conference 2023, 1.Events
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