Linear and nonlinear system identification techniques for modelling of a remotely operated underwater vehicle

S. M. Ahmad*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Scopus citations

Abstract

As opposed to classical mathematical-based modelling approach, this paper reports a black-box system identification technique for characterising the dynamics of a remotely operated vehicle (ROV). A linear system identification technique is employed to model the vehicle dynamics. However, use is also made of advance neural networks-based nonlinear system identification approach to model rudder-depth channel nonlinear behaviour. Different model validity tests are also employed to instil confidence in the identified linear and nonlinear ROV dynamic models. High fidelity models obtained for the multi-degree-of-freedom vehicle are of immense importance for developing ROV simulators, pilot training and autopilot design.

Original languageEnglish
Pages (from-to)75-87
Number of pages13
JournalInternational Journal of Modelling, Identification and Control
Volume24
Issue number1
DOIs
StatePublished - 1 Sep 2015
Externally publishedYes

Bibliographical note

Publisher Copyright:
Copyright © 2015 Inderscience Enterprises Ltd.

Keywords

  • Linear system identification
  • Neural networks
  • Remotely operated underwater vehicle modelling

ASJC Scopus subject areas

  • Modeling and Simulation
  • Computer Science Applications
  • Applied Mathematics

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