This thesis examines the application of artificial neural networks (NNs) to the problem of dynamic flight control. The specific application is the control of a flying model helicopter. The control interface is provided through a hardware and software test bed called the Fast Adaptive Maneuvering Experiment (FAME). The NN design approach uses two NNs: one trained as an emulator of the plant and the other trained to control the emulator. The emulator neural network is designed to reproduce the flight dynamics of the experimental plant. The controller is then designed to produce the appropriate control inputs to drive the emulator to a desired final state. Previous research in the area of NNs for controls has almost exclusively been applied to simulations. To develop a controller for a real plant, a neural network must be created which will accurately recreate the dynamics of the plant. This thesis demonstrates the ability of a neural network to emulate a real, dynamic, nonlinear plant. Neural networks, Helicopter control, Fast Adaptive Maneuvering Experiment (FAME).
Neural Networks for Dynamic Flight Control
1993
72 pages
Report
No indication
English
Adaptive Flight Control Using Dynamic Inversion and Neural Networks
British Library Conference Proceedings | 1999
|Neural Networks for Flight Control
NTIS | 1996
|Neural Networks for Flight Control
British Library Conference Proceedings | 1996
|