Control on 3-D Fixable Wing Flutter Using an Adaptive Neural Controller

Abstract

An adaptive neural controller to control on flutter in 3-D flexible wing is proposed. The aeroelastic model was based on the coupling between structure-of the equivalent plate (wing) and the aerodynamic model that is based on a hybrid unsteady panel methodTime domain simulations were used to examine the dynamic aeroelastic instabilities of the system (e.g. the onset of flutter and limit cycle oscillation). The structure of the controller consists of two models namely modified Elman neural network (MENN) and feedforward multi-layer Perceptron (MLP). The MENN model is trained with off-line and on-line stages to guarantee that the outputs of the model accurately represent the plunge motion of the wing and this neural model acts as the identifier. The feedforward neural controller is trained off-line and adaptive weights are implemented on-line to find the generalized control action (function of addition lift force), which controls the plunge motion of the wing. The general back propagation algorithm is used to learn the feedforward neural controller and the neural identifier. The simulation results show the effectiveness of the proposed control algorithm; this is demonstrated by the minimized tracking error to zero approximation with very acceptable settling time.

Keywords

• aeroelasticity
• flutter
• adaptive control
• neural networks