Neural network augmented identification of underwater vehicle models

Pepijn W.J. Van De Ven, Tor A. Johansen, Asgeir J. Sørensen, Colin Flanagan, Daniel Toal

Research output: Contribution to journalConference articlepeer-review

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.

Original languageEnglish
Pages (from-to)263-268
Number of pages6
JournalIFAC Proceedings Volumes (IFAC-PapersOnline)
Volume37
Issue number10
DOIs
Publication statusPublished - 2004
Externally publishedYes
Event2004 IFAC Conference on Computer Applications in Marine Systems, CAMS 2004 - Ancona, Italy
Duration: 7 Jul 20049 Jul 2004

Keywords

  • Autonomous vehicles
  • Back propagation
  • Marine systems. neural networks
  • Nonlinear systems
  • System identification
  • Time-varying systems

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