The application of the self-tuning neural network PID controller on the ship roll reduction in random waves

Ming-Chung Fang, Young Zoung Zhuo, Zi Yi Lee

Research output: Contribution to journalArticle

68 Citations (Scopus)

Abstract

In this paper, we present a mathematical model including seakeeping and maneuvering characteristics to analyze the roll reduction for a ship traveling with the stabilizer fin in random waves. The self-tuning PID controller based on the neural network theory is applied to adjust optimal stabilizer fin angles to reduce the ship roll motion in waves. Two multilayer neural networks, including the system identification neural network (NN1) and the parameter self-tuning neural network (NN2), are adopted in the study. The present control technique can save the time for searching the optimal PID gains in any sea states. The simulation results show that the present developed self-tuning PID control scheme based on the neural network theory is indeed quite practical and sufficient for the ship roll reduction in the realistic sea.

Original languageEnglish
Pages (from-to)529-538
Number of pages10
JournalOcean Engineering
Volume37
Issue number7
DOIs
Publication statusPublished - 2010 May 1

Fingerprint

Ships
Tuning
Neural networks
Controllers
Circuit theory
Seakeeping
Three term control systems
Multilayer neural networks
Identification (control systems)
Mathematical models

All Science Journal Classification (ASJC) codes

  • Environmental Engineering
  • Ocean Engineering

Cite this

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The application of the self-tuning neural network PID controller on the ship roll reduction in random waves. / Fang, Ming-Chung; Zhuo, Young Zoung; Lee, Zi Yi.

In: Ocean Engineering, Vol. 37, No. 7, 01.05.2010, p. 529-538.

Research output: Contribution to journalArticle

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