Abstract
We investigated the design of a neural‐network‐based adaptive control system for a smart structural dynamic model of the twin tails of an F‐15 tail section. A neural network controller was developed and tested in computer simulation for active vibration suppression of the model subjected to parametric excitation. First, an emulator neural network was trained to represent the structure to be controlled and thus used in predicting the future responses of the model. Second, a neurocontroller to determine the necessary control action on the structure was developed. The control was implemented through the application of a smart material actuator. A strain gauge sensor was assumed to be on each tail. Results from computer‐simulation studies have shown great promise for control of the vibration of the twin tails under parametric excitation using artificial neural networks.
Highlights
In aeroelastic investigations of aircraft, the structural behavior of aircraft components is assumed to be linear [1]
The problem at hand is to suppress the vibrations of a structural dynamic model (1/16 dynamically scaled) of the twin tails of the F-15 fighter plane
A prime advantage of the multilayer perceptrons (MLPs) neural network is its capability to generate the steady-state characteristics of the dynamic process identified
Summary
In aeroelastic investigations of aircraft, the structural behavior of aircraft components is assumed to be linear [1]. One of the available techniques that can be used to solve critical structural problems on fixed-wing aerospace vehi- Another interesting application for neural networks is active vibration control of smart structures. One of the main objectives is to deal with imprecise mathematical models due to unmodeled dynamics and remove the requirement of having an exact detailed mathematical model for the system which can be a very time consuming process This is where the power of neural networks is stressed; it can identify the system using the true input/output data without any prior model information. It has been shown that multilayer perceptrons (MLPs) are universal function approximators [8] Later this model type has been used to train neurocontrollers to suppress the vibrations of nonlinear smart structures. Illustrative examples of the backpropagation algorithm can be found in [7]
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