A Novel Dual Fuzzy Neural Network to Civil Aviation Aircraft Disturbance Landing Control

Abstract

A novel dual fuzzy neural network to civil aviation aircraft disturbance landing control is presented in this paper. Conventional automatic landing system (ALS) can provide a smooth landing, which is essential to the comfort of passengers. However, these systems work only within a specified operational safety envelope. When the conditions are beyond the envelope, such as turbulence or wind shear, they often cannot be used. The objective of this paper is to investigate the use of dual fuzzy neural network in ALS and to make that system more intelligent. Firstly the dual fuzzy neural network is trained from available flight data and then that trained neural network controls the landing, roll, pitch and altitude hold of the airplane. Current flight control law is adopted in the intelligent controller design. Tracking performance and robustness are demonstrated through software simulations. The neural network control has been implemented in MATLAB and the data for training have been taken from Flight Gear Simulator. Simulated results show that control for different flight phases is successful and the dual fuzzy neural network controllers provide the robustness to system parameter variation.