• DocumentCode
    2067461
  • Title

    Study on ship steering based on hybrid intelligent control

  • Author

    YANG, Guoxun ; GUO, Chen ; Jia, Xinle

  • Author_Institution
    Lab of Simulation & Control of Navigation Syst., Dalian Maritime Univ., China
  • Volume
    3
  • fYear
    2002
  • fDate
    2002
  • Firstpage
    2118
  • Abstract
    Hybrid intelligent technique is used in ship steering control. It can make full use of the advantages of all kinds of intelligent algorithms. This provides an efficient way for the improvement of ship steering control performance. In this paper, genetic algorithm optimization is used in off-line learning period. According to the new definition of fitness function, the optimized result obtained is more suitable to the actual situation. In online learning period, reinforcement learning and neural fuzzy control are integrated. It removes the defect that the conventional hybrid intelligent algorithm learning must be provided with some sample data, and. the ship control quality is effectively improved in the case of appending additional sea state disturbance.
  • Keywords
    fuzzy control; genetic algorithms; intelligent control; learning (artificial intelligence); neurocontrollers; position control; ships; fitness function; fuzzy control; genetic algorithm; hybrid intelligent control; neural control; optimization; reinforcement learning; ship; steering control; Control system synthesis; Fuzzy control; Fuzzy neural networks; Intelligent control; Intelligent robots; Marine vehicles; Navigation; Neural networks; Supervised learning; Uncertainty;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    American Control Conference, 2002. Proceedings of the 2002
  • ISSN
    0743-1619
  • Print_ISBN
    0-7803-7298-0
  • Type

    conf

  • DOI
    10.1109/ACC.2002.1023949
  • Filename
    1023949