A novel optimized artificial neural networks-based controller for smart morphing wings

Morphing wings represent a major advancement in aerospace engineering, capable of dynamically altering shape during flight to enhance performance and reduce fuel consumption. This study introduces a novel methodology for controlling morphing wings using Macro Fiber Composite (MFC) actuators configured in parallel at the trailing edge. By electrically stimulating the MFC actuators, we achieve continuous wing shape modulation, with a maximum deflection of 17 mm and a resultant force of 6.55 N. To optimize morphing control, we developed an Artificial Neural Network (ANN) based controller, trained on data from a detailed Finite Element (FE) model using Ansys Parametric Design Language (APDL). The ANN controller accurately predicts the required control voltages to achieve desired wing shapes, enhancing maneuverability and reducing wing weight. Our results demonstrate the feasibility of this approach, with the ANN controller achieving an accuracy of 89.9%. This research underscores the practical advantages of ANN-based controllers in morphing wing applications, offering a promising alternative to traditional wing control methods and contributing to improved aircraft performance and environmental sustainability.