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Optimized Multi-cascade Fuzzy Model for Ship Dynamic Positioning System Based on Genetic Algorithm

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Industrial Networks and Intelligent Systems (INISCOM 2019)

Abstract

In this paper, we aim to develop a multi-cascade fuzzy model for the ship dynamic positioning system influenced by environment to enhance its quality. The cascades of fuzzy model are selected corresponding to the level of output feedback error. The optimized tuning of the structure parameter for fuzzy-case 2 and fuzzy-case 4 is realized by the genetic algorithm. Then, the simulation studies which compare our proposed control strategy with fuzzy control strategy using Matlab are carried out, and the simulation result proves the effectiveness of the multi-cascade fuzzy model.

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Author information

Authors and Affiliations

  1. Ho Chi Minh City University of Transport, Ho Chi Minh City, Vietnam

    Viet-Dung Do & Xuan-Kien Dang

  2. Dong An Polytechnic, Di An, Vietnam

    Viet-Dung Do

  3. SaiGon University, Ho Chi Minh City, Vietnam

    Leminh-Thien Huynh & Van-Cuu Ho

Authors
  1. Viet-Dung Do
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  2. Xuan-Kien Dang
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  3. Leminh-Thien Huynh
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  4. Van-Cuu Ho
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Corresponding author

Correspondence to Viet-Dung Do .

Editor information

Editors and Affiliations

  1. Queen's University Belfast, Belfast, UK

    Trung Quang Duong

  2. Faculty of Electrical and Electronics Engineering, Duy Tan University, Da Nang, Vietnam

    Nguyen-Son Vo

  3. Nong Lam University Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Loi K. Nguyen

  4. School of Science and Technology, Middlesex University, London, UK

    Quoc-Tuan Vien

  5. School of Electronic Engineering, Soongsil University, Seoul, Korea (Republic of)

    Van-Dinh Nguyen

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Do, VD., Dang, XK., Huynh, LT., Ho, VC. (2019). Optimized Multi-cascade Fuzzy Model for Ship Dynamic Positioning System Based on Genetic Algorithm. In: Duong, T., Vo, NS., Nguyen, L., Vien, QT., Nguyen, VD. (eds) Industrial Networks and Intelligent Systems. INISCOM 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 293. Springer, Cham. https://doi.org/10.1007/978-3-030-30149-1_14

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  • DOI: https://doi.org/10.1007/978-3-030-30149-1_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-30148-4

  • Online ISBN: 978-3-030-30149-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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