Identification of a Variable Mass Underwater Vehicle Via Volterra Neural Network-Reference-Cited by-同舟云学术

Identification of a Variable Mass Underwater Vehicle Via Volterra Neural Network

Published:2010-02-02 Issue:2 Volume:132 Page:
ISSN:0022-0434
Container-title:Journal of Dynamic Systems, Measurement, and Control
language:en
Short-container-title:

Author:

Binazadeh T.¹,Yazdanpanah M. J.¹,Shafiei M. H.¹

Affiliation:

1. Control & Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran P.O. Box 14395/515

Abstract

The first step in designing a control system for a rigid body is to understand its dynamics. Underwater vehicle dynamics may be complex and difficult to model, mainly due to difficulties in observing and measuring actual underwater vehicle hydrodynamics response. This paper is concerned with structure selection of nonlinear polynomials in a Volterra polynomial basis function neural network and recursive parameter estimation of the selected model, in order to obtain a model of a variable mass underwater vehicle with six degrees of freedom using an input-output data set. The simulation results reveal the efficiency of the approach.

Publisher

ASME International

Subject

Computer Science Applications,Mechanical Engineering,Instrumentation,Information Systems,Control and Systems Engineering

Link

http://asmedigitalcollection.asme.org/dynamicsystems/article-pdf/doi/10.1115/1.4000814/5782179/024501_1.pdf

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