Abstract:
This thesis investigates how two neural network-based control techniques can be applied
to a specific spacecraft control problem.
The neural networks used are simple backpropagation networks, consisting of one or
more tansigmoidal neurons (neurons with tanh transfer functions) in a hidden layer, and a
linear neuron in the output layer. The neural network control techniques investigated
here are Direct Model Inversion and Indirect Model Inversion.
The spacecraft control problem is that of reducing the vibrations of a spacecraft payload.
The source of the vibrations is a mass imbalance in one of the reaction wheels of the
spacecraft. Four components are represented in the spacecraft model. These are rigid
body inertia, solar array flexure, fuel slosh and payload vibration. A simple sinusoidal
signal is used to model the disturbance torque produced by the reaction wheel mass
imbalance. The complete model is broadly based on the Solar Heliospheric Observatory
(SOHO) that is due for launch in 1995.
Each of the neural network control techniques used is shown to be successful in reducing
the effects of the disturbance torques on the spacecraft payload. However, in each case,
a simple positional feedback gain term provides more effective and reliable control.
