Type of Document	Dissertation
Author	El-Badawy, Ayman Aly
Author's Email Address	abadawy@mailcity.com
URN	etd-04112000-13090021
Title	Structural Identification and Buffet Alleviation of Twin-Tailed Fighter Aircraft
Degree	PhD
Department	Mechanical Engineering
Advisory Committee	Advisor Name Title Ali H. Nayfeh Committee Chair Dean T. Mook Committee Member Gordon Kirk Committee Member Harley H. Cudney Committee Member Mehdi Ahmadian Committee Member William R. Saunders Committee Member
Keywords	Buffet Smart Structures Neural Networks Nonlinear Dynamics Perturbation Fighter Aircraft Active Control
Date of Defense	2000-03-30
Availability	unrestricted
Abstract We tackle the problem of identifying the structural dynamics of the twin tails of the F-15 fighter plane. The objective is to first investigate and identify the different possible attractors that coexist for the same operating parameters. Second is to develop a model that simulates the experimentally determined dynamics. Third is to suppress the high-amplitude vibrations of the tails due to either principal parametric or external excitations. To understand the dynamical characteristics of the twin-tails, the model is excited parametrically. For the same excitation amplitude and frequency, five different responses are observed depending on the initial conditions. The coexisting five responses are the result of the nonlinearities. After the experimental identification of the system, we develop a model to capture the dynamics realized in the experiment. We devise a nonlinear control law based on cubic velocity feedback to suppress the response of the model to a principal parametric excitation. The performance of the control law is studied by comparing the open- and closed-loop responses of the system. Furthermore, we conduct experiments to verify the theoretical analysis. The theoretical and experimental findings indicate that the control law not only leads to effective vibration suppression, but also to effective bifurcation control. We investigate the design of a neural-network-based adaptive control system for active vibration suppression of the model when subjected to a parametric excitation. First, an emulator neural network was trained to represent the structure and thus used to predict the future responses of the model. Second, a neurocontroller is developed to determine the necessary control action. The computer-simulation studies show great promise for artificial neural networks to control the model vibrations caused by parametric excitations. We investigate the use of four different control strategies to suppress high-amplitude responses of the F-15 fighter to a primary resonance excitation. The control strategies are linear velocity feedback, nonlinear velocity feedback, positive position feedback, and saturation-based control. For each case, we conduct bifurcation analyses for the open- and closed-loop responses of the system and investigate theoretically the performance of the different control strategies. We also calculate the instantaneous power requirements of each control law. The experimental results agree with the theoretical findings.
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