Peer-Reviewed Journal Details
Mandatory Fields
Van-de-Ven PWJ, Johansen TA, Sorensen AJ, Flanagan C, Toal D
2007
June
Control Engineering Practice
Neural network augmented identification of underwater vehicle models
Published
()
Optional Fields
autonomous vehicles back propagation marine systems neural networks nonlinear systems system identification ROBOTIC VEHICLES LEARNING CONTROL NET CONTROLLER CONTROL-SYSTEM
15
6
715
725
In this article the use of neural networks in the identification of models for underwater vehicles is discussed. Rather than using a neural network in parallel with the known model to account for unmodelled 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 in simulations using the neural networks in a feed forward controller. The advantages of online learning are shown in case of noise impaired measurements and changing dynamics due to a change in toolskid. (c) 2005 Elsevier Ltd. All rights reserved.
ISSN: 0967-0661
10.1016/j.conengprac.2005.11.004
Grant Details
IRCSET PhD