A Neural Network Controller New Methodology for the ATR-42 Morphing Wing Actuation

Abstract

A morphing wing model is used to improve aircraft performance. To obtain the desired airfoils, electrical actuators are used, which are installed inside of the wing to morph its upper surface in order to obtain its desired shape. In order to achieve this objective, a robust position controller is needed. In this research, a design and test validation of a controller based on neural networks is presented. This controller was composed by a position controller and a current controller to manage the current consumed by the electrical actuators to obtain its desired displacement. The model was tested and validated using simulation and experimental tests. The results obtained with the proposed controller were compared to the results given by the PID controller. Wind tunnel tests were conducted in the Price-Païdoussis Wind Tunnel at the LARCASE laboratory in order to calculate the pressure coefficient distribution on an ATR-42 morphing wing model for different flow conditions. The pressure coefficients obtained experimentally were compared with their numerical values given by XFoil software.

Keywords

• Neural networks
• morphing wing
• controller
• electrical actuator modeling
• wind tunnel