Bottom profiling control of an UUV using neural networks

Artificial neural networks offer an alternative strategy for the nonlinear control of unmanned underwater vehicles (UUVs). This paper investigates the use of a multi-layered perceptron (MLP) network in controlling an UUV over a sea-bed profile and compares the use of applying chemotaxis learning over that of the more commonly employed backpropagation algorithm. The results show for differing sized MLPs the chemotaxis algorithm produces a successful controller over the sea bed profile in an improved training time. To further vindicate the chemotaxis network, it was then presented with the problem of meeting a new profile to travel over. The results show from several simulation runs that the chemotaxis network provides a robust controller over numerous sea bed profiles of which it had no prior knowledge