A Neural Network Approach to Predicting Airspeed in Helicopters

Abstract

A helicopter's airspeed and sideslip angle is difficult to measure at speeds below 50 knots. This paper describes the application of Artificial Neural Network (ANN) techniques to the helicopter low airspeed problem. Three ANN methods were applied to the problem: a linear network, a Radial Basis Function (RBF) network, and a Multi-Layer Perceptron (MLP), trained using an implementation of the Levenberg-Marquardt (L-M) algorithm. Internally available measurements, such as control positions and body attitudes and rates, were generated using a realistic simulation model of a Lynx helicopter. These measurements formed the inputs to the ANN methods. The MLP was found to be the superior method. Further testing, including a Taguchi analysis, indicated the validity of the method. It is concluded that ANN techniques present a promising solution to the helicopter low airspeed problem.