Neural network augmented identification of underwater vehicle models

Abstract

In this article the use of neural networks in models for underwater vehicles is discussed. Rather than using a neural network parallel to the known model to account for unmodeled phenomena in a model wide fashion, knowledge regarding the various parts of the model is used to apply neural networks for those parts of the model that are most uncertain. As an example, the damping of an underwater vehicle is identified using neural networks. The performance of the neural network based model is demonstrated for an AUV that changes its physical characteristics during a simulated intervention operation