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Technical Briefs

Identification of a Variable Mass Underwater Vehicle Via Volterra Neural Network

[+] Author and Article Information
T. Binazadeh

Control & Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran P.O. Box 14395/515binazadeh@ut.ac.ir

M. J. Yazdanpanah

Control & Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran P.O. Box 14395/515yazdan@ut.ac.ir

M. H. Shafiei

Control & Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran P.O. Box 14395/515shafiei@ut.ac.ir

J. Dyn. Sys., Meas., Control 132(2), 024501 (Feb 02, 2010) (7 pages) doi:10.1115/1.4000814 History: Received September 08, 2008; Revised November 18, 2009; Published February 02, 2010; Online February 02, 2010

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.

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Copyright © 2010 by American Society of Mechanical Engineers
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Figures

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Figure 1

Inertia-frame and body-frame coordinate systems

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Figure 2

Horizontal and vertical fins

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Figure 3

Input-output relations

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Figure 4

Real and estimated output of the first block based on (a) only offline identification and (b) online identification

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Figure 5

Real and estimated output of the second block based on (a) only offline identification and (b) online identification

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Figure 6

Real and estimated output of the third block based on (a) only offline identification and (b) online identification

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