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.