Real-time geometrical approximation of flexible structures using neural networks

Abstract

This study demonstrates the potential of artificial neural networks for the geometrical approximation of flexible structures. Online modeling of the deformation and dynamics of flexible structures can improve the control and performance of systems such as airplane wings, rotor blades of helicopters, large articulated space structures, and robots with flexible links or joints. Here a neural model that approximates the deflection of such structures is developed. Real-time modeling is provided by a specialized neural network processor. We demonstrate this concept using the model for the nonlinear deflection of an viscoelastic airplane wing.