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Estimation of water momentum and propeller velocity in bow thruster model of autonomous surface vehicle using modified Kalman filter

International Journal of Electrical and Computer Engineering (IJECE) · Vol. 12 No. 6 (2022)

DOI
10.11591/ijece.v12i6.pp5988-5997
ISSN
2722-2578, 2088-8708
Published
2022-12-01
Publisher
Institute of Advanced Engineering and Science
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Autonomous surface vehicle (ASV) is a vehicle in the form of an unmanned on-water surface vessel that can move automatically. As such, an automatic control system is essentially required. The bow thruster system functions as a propulsion control device in its operations. In this research, the water momentum and propeller velocity were estimated based on the dynamic bow thruster model. The estimation methods used is the Kalman filter (KF) and ensemble Kalman filter (EnKF). There are two scenarios: tunnel thruster condition and open-bladed thruster condition. The estimation results in the tunnel thruster condition showed that the root mean square error (RMSE) by the EnKF method was relatively smaller, that is, 0.7920 and 0.1352, while the estimation results in the open-bladed thruster condition showed that the RMSE by the KF method was relatively smaller, that is, 1.9957 and 2.0609.

Collaboration network of the authors

10 authors · 18 co-authorship links · 7 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Institut Teknologi Sepuluh Nopember
  • Universitas Nahdlatul Ulama Surabaya
  • Darul Ulum Islamic University
  • Universitas Muhammadiyah Malang
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Erna AprilianiInstitut Teknologi Sepuluh Nopember3
Imam MukhlashInstitut Teknologi Sepuluh Nopember1
Acep TaryanaInstitut Teknologi Sepuluh Nopember1
Muhammad Syifa’ul MufidInstitut Teknologi Sepuluh Nopember1
Dinita RahmaliaDarul Ulum Islamic University1
Thomy Eko SaputroUniversitas Muhammadiyah Malang1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

This record comes from Crossref (what the publisher deposited), because the journal's own OAI-PMH endpoint could not be harvested.

Metadata source: Crossref API (cadangan, berdasarkan ISSN) https://api.crossref.org/journals/2088-8708/works, first harvested 27 Okt 2026, last seen 1 Nov 2026.
Metadata as oai_dc, converted from Crossref
<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/"><dc:title>Estimation of water momentum and propeller velocity in bow thruster model of autonomous surface vehicle using modified Kalman filter</dc:title><dc:creator>Nurhadi, Hendro</dc:creator><dc:creator>Kiki, Mayga</dc:creator><dc:creator>Adzkiya, Dieky</dc:creator><dc:creator>Herlambang, Teguh</dc:creator><dc:description>&lt;jats:p&gt;&amp;lt;span lang="EN-US"&amp;gt;Autonomous surface vehicle (ASV) is a vehicle in the form of an unmanned on-water surface vessel that can move automatically. As such, an automatic control system is essentially required. The bow thruster system functions as a propulsion control device in its operations. In this research, the water momentum and propeller velocity were estimated based on the dynamic bow thruster model. The estimation methods used is the Kalman filter (KF) and ensemble Kalman filter (EnKF). There are two scenarios: tunnel thruster condition and open-bladed thruster condition. The estimation results in the tunnel thruster condition showed that the root mean square error (RMSE) by the EnKF method was relatively smaller, that is, 0.7920 and 0.1352, while the estimation results in the open-bladed thruster condition showed that the RMSE by the KF method was relatively smaller, that is, 1.9957 and 2.0609.&amp;lt;/span&amp;gt;&lt;/jats:p&gt;</dc:description><dc:publisher>Institute of Advanced Engineering and Science</dc:publisher><dc:date>2022-12-01</dc:date><dc:type>info:eu-repo/semantics/article</dc:type><dc:format>application/pdf</dc:format><dc:identifier>https://doi.org/10.11591/ijece.v12i6.pp5988-5997</dc:identifier><dc:identifier>10.11591/ijece.v12i6.pp5988-5997</dc:identifier><dc:source>International Journal of Electrical and Computer Engineering (IJECE); Vol 12 No 6 (2022); 5988</dc:source><dc:source>2722-2578</dc:source><dc:source>2088-8708</dc:source><dc:relation>https://ijece.iaescore.com/index.php/IJECE/article/viewFile/28724/16042</dc:relation><dc:rights>http://creativecommons.org/licenses/by-sa/4.0</dc:rights></oai_dc:dc>