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Artificial intelligence-neuro fuzzy method for modeling the unsteady aerodynamics of an aircraft
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DOI：10.1177/00202940251407224.png)
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Abstract
En 中文
Unsteady aerodynamics can significantly affect the design of highly maneuverable aircraft that fly at high angles of attack. Conventional modeling methods, such as polynomial methods or lookup tables, are of limited value. This investigation was motivated by the need for an accurate and simplified prediction of unsteady aerodynamics by modeling experimental data. Such modeling is important for preliminary aircraft design purposes and control law development, which is a challenging problem when considering a highly nonlinear aerodynamic loading model. In this study, a neuro-fuzzy (NF) model developed using experimental data was favorably compared with other modeling techniques and semi-empirical methods. The model uses three inputs (angle of attack, non-dimensional pitch rate, and aspect ratio) and outputs the aerodynamic normal force coefficient, which was found with a root mean square error of less than 6% for an angle of attack range of 0 degrees-90 degrees. The proposed NF modeling procedure is demonstrated and recommended for use with other aerodynamic force coefficients as the new model outputs. The minimum and maximum angles of attack should be used as model inputs to improve model accuracy for multiple ranges of angles of attack.
Keywords:
AI
neuro-fuzzy
angle of attack
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