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A new system for underwater vehicle balancing control based on weightless neural network and fuzzy logic methods

IAES International Journal of Artificial Intelligence · Vol. 13 No. 3 (2024) · hlm. 2870–2882

DOI
10.11591/ijai.v13.i3.pp2870-2882
ISSN
2252-8938, 2089-4872
Terbit
2024-09-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

The utilization of humans to be in the water for short time, resulting in limited area underwater that can be explored, so the information obtained is very limited, plus the influence of irregular water movements, changes in waves, and changes in water pressure, indirectly also constitutes obstacle to this problem. One of the best solutions is to develop underwater vessel that can travel either autonomously or by giving control of movement and navigation systems. New system for underwater vehicle balance control through weightless neural network (WNN) and fuzzy logic methods was proposed in this study. The aim was to simplify complicated data source on stability system using WNN algorithm and determine depth level of autonomous underwater vehicle (AUV) through fuzzy logic method. Moreover, speed control of underwater vehicle was determined using fuzzy rule-based design and inference. The tests were conducted by showing convergence performance of system in the form of AUV simulator. The results showed that proposed system could produce real-time motion balance performance, faster execution time, and good level of accuracy. This study was expected to produce real-time motion balance system with better performance, faster execution time, and good level of accuracy which could be subsequently used to design simple, cheap, and efficient hardware prototype.

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  • Sriwijaya University
  • Samudra University
  • Universitas Dinamika Bangsa
  • University of Technology Malaysia
  • Al Baha University
  • Universitas Halu Oleo
  • Politeknik Negeri Sriwijaya
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Rekan penulisOrganisasiArtikel bersama
Deris StiawanSriwijaya University7
Siti NurmainiSriwijaya University3
Rahmat BudiartoAl Baha University2
Mohd. Yazid IdrisUniversity of Technology Malaysia2
Yoppy SazakiSriwijaya University2
Muhammad SyahroyniSriwijaya University2
Bambang TutukoSriwijaya University2
Annisa DarmawahyuniSriwijaya University2
Bhakti Yudho SupraptoSriwijaya University2
Fitriani FitrianiUniversitas Halu Oleo1
Hadita AmilaSamudra University1
Ramaidhani RamaidhaniSamudra University1
Ade Iriani SapitriSriwijaya University1
Muhammad Naufal RachmatullahSriwijaya University1
Firdaus FirdausSriwijaya University1
Anggun IslamiSriwijaya University1
Samsuryadi SamsuryadiSriwijaya University1
Sharipuddin SharipuddinUniversitas Dinamika Bangsa1
Benni PurnamaUniversitas Dinamika Bangsa1
Darmawijoyo HanapiSriwijaya University1
Rizky Elinda SariSriwijaya University1
Huda UbayaSriwijaya University1
Rivaldi Febrian EkaputraAfiliasi tidak diketahui1
Septiani Kusuma NingrumSriwijaya University1
Winda Kurnia SariSriwijaya University1
Jordan MarcelinoSriwijaya University1
Rendy IsdwantaSriwijaya University1
Cholidah Zuhroh KhairunnisaSriwijaya University1
Fathoni FathoniSriwijaya University1
Indra Griha Tofik IsaNational Chin-Yi University of Technology1
Egga AsokaPoliteknik Negeri Sriwijaya1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">A new system for underwater vehicle balancing control based on weightless neural network and fuzzy logic methods</dc:title>
	<dc:creator>Zarkasi, Ahmad</dc:creator>
	<dc:creator>Satria, Hadipurnawan</dc:creator>
	<dc:creator>Primanita, Anggina</dc:creator>
	<dc:creator>Abdurahman, Abdurahman</dc:creator>
	<dc:creator>Afifah, Nurul</dc:creator>
	<dc:creator>Sutarno, Sutarno</dc:creator>
	<dc:subject xml:lang="en-US">Embedded System and Artificial Intelligence</dc:subject>
	<dc:subject xml:lang="en-US">Balance control system; Fuzzy logic; Underwater vehicle; Unmanned aerial vehicles; Weightless neural network</dc:subject>
	<dc:description xml:lang="en-US">The utilization of humans to be in the water for short time, resulting in limited area underwater that can be explored, so the information obtained is very limited, plus the influence of irregular water movements, changes in waves, and changes in water pressure, indirectly also constitutes obstacle to this problem. One of the best solutions is to develop underwater vessel that can travel either autonomously or by giving control of movement and navigation systems. New system for underwater vehicle balance control through weightless neural network (WNN) and fuzzy logic methods was proposed in this study. The aim was to simplify complicated data source on stability system using WNN algorithm and determine depth level of autonomous underwater vehicle (AUV) through fuzzy logic method. Moreover, speed control of underwater vehicle was determined using fuzzy rule-based design and inference. The tests were conducted by showing convergence performance of system in the form of AUV simulator. The results showed that proposed system could produce real-time motion balance performance, faster execution time, and good level of accuracy. This study was expected to produce real-time motion balance system with better performance, faster execution time, and good level of accuracy which could be subsequently used to design simple, cheap, and efficient hardware prototype.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">The research publication of this article was funded by DIPA of Public Service Agency of Universitas Sriwijaya 2022. Nomor SP DIPA-023.17.2.677515/2022, On November 17, 2021. In accordance with the Dean's Decree Number: 2670/UN9.1.9/LT/2022</dc:contributor>
	<dc:date>2024-09-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/24733</dc:identifier>
	<dc:identifier>10.11591/ijai.v13.i3.pp2870-2882</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 3: September 2024; 2870-2882</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v13.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/24733/14095</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2024 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>