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Shock and Vibration
Volume 7, Issue 6, Pages 355-361

Neural Network Identification and Control of a Parametrically Excited Structural Dynamic Model of an F-15 Tail Section

Ayman A. El-Badawy, Ali H. Nayfeh, and Hugh Van Landingham

Virginia Polytechnic Institute and State University, Blacksburg, VA 24061, USA

Received 24 August 1999; Revised 24 July 2000

Copyright © 2000 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.