Artificial neural networks for predicting nonlinear dynamic helicopter loads

Abstract

This paper proposes using an artificial neural network (ANN) to predict the loads in critical components based on flight variable information that can be easily measured. The artificial neural network learns the relationship between flight variables and component loads through exposure to a database of flight variable records and corresponding load histories taken from an instrumented military helicopter undergoing standard maneuvers. Eight standard flight variables are used as inputs for predicting the time varying mean and oscillatory components of the tailboom bending load and the pitch link load for seven flight maneuvers