• DocumentCode
    2045450
  • Title

    Research on ship slanting rudder anti-pitching intelligent adaptive Generalized Predictive Control

  • Author

    Hongli Chen ; Luo Gong ; Xiaojing Xia

  • Author_Institution
    Acad. of Autom., Harbin Eng. Univ., Nantong, China
  • fYear
    2015
  • fDate
    2-5 Aug. 2015
  • Firstpage
    1619
  • Lastpage
    1623
  • Abstract
    This article presents a method to build a T-S fuzzy model based on Generalized Dynamic Fuzzy Neural Network (GD-FNN) of Elliptical Basis Function in order to solve the problem of ship motion model´s uncertainty and nonlinearity. The proposed method needs neither prior fuzzy neural networks structure knowledge nor prior training phase, it can be used to build the nonlinear and uncertain part through online adaptive learning algorithm. The fuzzy rules could be generated and pruned on-line by learning. The ship vertical (heave and pitch) dynamic linear adaptive CARMA model can be got by local dynamic linearization at each sampling point. Then Generalized Predictive Control (GPC) law is deduced by combining adaptive linear model with generalized predictive control. Simulation experiment shows that this algorithm is effective and efficient, its anti-pitching effect reaches 82.2%.
  • Keywords
    adaptive control; control nonlinearities; fuzzy control; learning systems; linear systems; neurocontrollers; predictive control; ships; uncertain systems; GD-FNN; GPC law; T-S fuzzy model; antipitching intelligent adaptive generalized predictive control; dynamic linear adaptive CARMA model; elliptical basis function; generalized dynamic fuzzy neural network; local dynamic linearization; model nonlinearity; model uncertainty; online adaptive learning algorithm; ship motion model; ship slanting rudder; Adaptation models; Adaptive systems; Dynamics; Marine vehicles; Mathematical model; Predictive control; Predictive models; Anti-pitching; GD-FNN; GPC; Slanting Rudder;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Mechatronics and Automation (ICMA), 2015 IEEE International Conference on
  • Conference_Location
    Beijing
  • Print_ISBN
    978-1-4799-7097-1
  • Type

    conf

  • DOI
    10.1109/ICMA.2015.7237727
  • Filename
    7237727