Title page for ETD etd-04112000-13090021

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
Nayfeh, Ali H. Committee Chair
Ahmadian, Mehdi Committee Member
Cudney, Harley H. Committee Member
Kirk, R. Gordon Committee Member
Mook, Dean T. Committee Member
Saunders, William R. Committee Member
  • Fighter Aircraft
  • Smart Structures
  • Buffet
  • Neural Networks
  • Active Control
  • Nonlinear Dynamics
  • Perturbation
Date of Defense 2000-03-30
Availability unrestricted
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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