Treffer: Neural network augmented identification of underwater vehicle models
Title:
Neural network augmented identification of underwater vehicle models
Authors:
Source:
Control engineering practice. 15(6):715-725
Publisher Information:
Oxford: Elsevier, 2007.
Publication Year:
2007
Physical Description:
print, 3/4 p
Original Material:
INIST-CNRS
Subject Terms:
Control theory, operational research, Automatique, recherche opérationnelle, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Automatique théorique. Systèmes, Control theory. Systems, Modélisation et identification, Modelling and identification, Robotique, Robotics, Ajustement modèle, Model matching, Ajustamiento modelo, Algorithme rétropropagation, Backpropagation algorithm, Algoritmo retropropagación, Boucle anticipation, Feedforward, Ciclo anticipación, Commande non linéaire, Non linear control, Control no lineal, Engin sous marin, Submarine vehicle, Artefacto submarino, Identification système, System identification, Identificación sistema, Modélisation, Modeling, Modelización, Robot mobile, Moving robot, Robot móvil, Réseau neuronal, Neural network, Red neuronal, Rétropropagation, Backpropagation, Retropropagacíon, Système autonome, Autonomous system, Sistema autónomo, Système incertain, Uncertain system, Sistema incierto, Autonomous vehicles, Marine systems, Neural networks, Nonlinear systems
Document Type:
Konferenz
Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Electronic and Computer Engineering, University of Limerick, Limerick, Ireland
Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway
Department of Marine Technology, Norwegian University of Science and Technology, Trondheim, Norway
Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway
Department of Marine Technology, Norwegian University of Science and Technology, Trondheim, Norway
ISSN:
0967-0661
Rights:
Copyright 2007 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Accession Number:
edscal.18670240
Database:
PASCAL Archive
Weitere Informationen
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.