Estimation of Flight Stability Derivatives using Neural Networks based Methods

Abstract

Flight stability derivatives are critical parameters in the design and control of aircraft, influencing overall stability and performance. Accurate estimation of these derivatives is essential for developing advanced flight control systems, aircraft design, and pilot training. Traditional methods for estimating flight stability derivatives often rely on complex mathematical models and wind tunnel testing, which can be costly and time-consuming. This thesis investigates the application of neural networks to efficiently and accurately estimate flight stability derivatives. Neural networks, with their ability to model nonlinear relationships and handle large datasets, offer a promising alternative to traditional methods. Using experimental and simulation data, this research aims to develop a robust framework for applying System Identification techniques, which refer to a set of mathematical tools and algorithms used to build mathematical models of dynamic systems, to fifth-generation fighter aircraft. Different neural network architectures, including FFNN, RNN, LSTM, and GRU, were tested and analyzed on static and dynamic NASA datasets. These approaches showed promising results, with LSTM performing the best based on established evaluation metrics such as Mean Squared Error (MSE) and Root Mean Squared Error (RMSE). Neural networks, in general, also performed better than traditional methods currently used in the industry. A comparative study with Kalman Filter showed that the neural networks were not only more efficient in terms of accuracy but also computationally faster by at least 60 times.