Aircraft Parameter Estimation using Feedforward Neural Networks With Lyapunov Stability Analysis

Abstract

Aerodynamic parameter estimation is critical in the aviation sector, especially in design and development programs of defense-military aircraft. In this paper, new results of the application of Artificial Neural Networks (ANN) to the field of aircraft parameter estimation are presented. The performances of Feedforward Neural Network (FFNN) with Backpropagation and FFNN with Backpropagation using Recursive Least Square (RLS) are investigated for aerodynamic parameter estimation. The methods are validated on flight data simulated using MATLAB implementations. The normalized Lyapunov energy functional has been used to derive the convergence conditions for both the ANN-based estimation algorithms. The estimation results are compared on the basis of performance metrics and computation time. The performance of FFNN-RLS has been observed to be approximately 10% better than FFNN-BPN. Simulation results from both algorithms have been found to be highly satisfactory and pave the way for further applications to real flight test data.