Artificial neural networks-extended great deluge model to predict actuators displacements for a morphing wing tip system

Abstract

Resin-based fiber composite materials have received attention in aerospace composite engineering, particularly in aircraft morphing structures, due to their high mechanical characteristics, such as stiffness, and because of their potential to highly reduce the structural mass of modern aircraft. Aircraft morphing is referred to as the ability of an aircraft’s surface to change its geometry in flight. The modelling of a dynamic morphing wing system is here studied. The morphing wing was controlled using four electric actuators situated inside of the wing model. The main role of these actuators was to modify the wing upper surface shape designed and manufactured with a flexible material, so that the laminar-to-turbulent flow transition point can move closer to the wing trailing edge, thus causing a minimum viscous drag, for various flow conditions. To determine the skin deflections in the four actuators points, both LVDT and dial indicator gages were positioned on the wing. Four Linear Variable Differential Transducers (LVDTs) were used to indicate the positions of the four actuators, and four Dial Indicators gages were positioned on the wing to measure the real deflections of the flexible composite skin in the four actuation points. The relationship between the Dial Indicators’ values and the LVDTs’ values for a same set-point command signal had a nondeterministic and unpredictable behavior (not a linear one). The values of the displacements given by the LVDTs were different than the values given by the Dial Indicators. In this paper, an Artificial Neural Network (ANN) model was investigated created with the aim to predict the displacements of the wing upper surface skin in real time using four actuators. The proposed model was trained using the Extended Great Deluge (EGD) algorithm.