Due to their highly non-linear hydrodynamics and the unknown environmental conditions in
which they operate, the control of Unmanned Underwater Vehicles (UUVs) poses serious
difficulties for most classical design methods.
This thesis investigates the use of Artificial Neural Networks (ANNs) when applied to the
modelling and control of a UUV whilst following a varying seabed terrain and at differing
surge velocities. Different control procedures are examined and their relative merits
discussed in relation to the problem.
The results of using an ANN to model the depth dynamics of the UUV at a single operating
point and using this network model to train an ANN controller by use of Error
Backpropagation are presented. In addition, a different control strategy is investigated,
whereby, an ANN controller is trained by using the full mathematical model of the UUV
and linear transfer function representations of its depth dynamics at specific operating
points. The relative performances of using a feedforward ANN controller and a recurrent
ANN controller are compared over different profiles as well as contrasting the relative
merits of using Error Backpropagation, Chemotaxis and Alopex, as applied to the controller
training task. In addition, the robustness of the optimum controller in the presence of
disturbances and missions over unfamiliar terrain and at varying surge velocities are
considered.The Royal Naval Engineering Colleg
