Open AccessNeural Network Identification and Control of a Parametrically Excited Structural Dynamic Model of an F‐15 Tail Section
Author(s) -
Ayman El-Badawy,
Ali H. Nayfeh,
Hugh Van Landingham
Publication year - 2000
Publication title -
shock and vibration
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.418
H-Index - 45
eISSN - 1875-9203
pISSN - 1070-9622
DOI - 10.1155/2000/530231
Subject(s) - artificial neural network , parametric statistics , control theory (sociology) , controller (irrigation) , vibration , actuator , computer science , adaptive control , engineering , parametric model , system identification , smart material , control engineering , control (management) , artificial intelligence , physics , acoustics , materials science , data modeling , mathematics , biology , statistics , database , agronomy , nanotechnology
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
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom