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Neural network-PID controller for roll fin stabilizer

Abstract

Fin stabilizers are very effective devices for controlling the ship roll motion against external wave-generated moments. Lift forces due to flow around fin with an angle of attack produce anti - roll moment. Therefore control of attack angle plays important role in reducing roll of ships. This paper presents results of using a combined neural network and PID for roll control of ship with small draught. Numerical results are given of around-fin flow analysis with considering free surface effect modelled by neural network and imposed to controlling loop. Hydraulic machinery constraints are also considered in the modelling. The obtained results show good performance of the controller in reducing roll amplitude in random seas. The approach can be used for any irregular sea conditions.

Keywords:

Fin stabilizer, neural network, PID control, restoring force

Details

Issue
Vol. 17 No. 2(64) (2010)
Section
Latest Articles
Published
22-11-2010
DOI:
https://doi.org/10.2478/v10012-010-0014-3
Licencja:
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Open Access License

This journal provides immediate open access to its content under the Creative Commons BY 4.0 license. Authors who publish with this journal retain all copyrights and agree to the terms of the CC BY 4.0 license.

 

Author Biographies

Fatemeh Hoseini Dadmarzi,
Amirkabir University of Technology, Faculty of Marine Technology



Parviz Ghadimi,
Amirkabir University of Technology, Faculty of Marine Technology



Babak Ommani,
Sharif University of Technology, Faculty of Mechanical Engineering



Authors

  • Hassan Ghassemi

    Amirkabir University of Technology, Faculty of Marine Technology
  • Fatemeh Hoseini Dadmarzi

    Amirkabir University of Technology, Faculty of Marine Technology
  • Parviz Ghadimi

    Amirkabir University of Technology, Faculty of Marine Technology
  • Babak Ommani

    Sharif University of Technology, Faculty of Mechanical Engineering

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