• DocumentCode
    2892814
  • Title

    Anfis Applied to a Ship Autopilot Design

  • Author

    Zhang, Xian-ku ; Jin, Yi-cheng ; Guo, Ge

  • Author_Institution
    Lab. of Marine Simulation & Control, Dalian Maritime Univ.
  • fYear
    2006
  • fDate
    13-16 Aug. 2006
  • Firstpage
    2233
  • Lastpage
    2236
  • Abstract
    Using a batch learning scheme and a hybrid learning rule, i.e. BP algorithm is applied to the learning of premise parameters, while least square algorithm to the learning of consequent parameters, an ANFIS system for ship autopilot with two inputs and one output, three fuzzy zones, nine fuzzy rules is trained. Training data come from a PD course control system, then the trained ANFIS autopilot controls an oil tanker that is described by a nonlinear ship model. The simulating results by Matlab indicate that the performance of ANFIS controller is similar to that of the training PD controller with good robustness
  • Keywords
    PD control; closed loop systems; control engineering computing; control system synthesis; fuzzy control; fuzzy reasoning; fuzzy systems; learning (artificial intelligence); neurocontrollers; remotely operated vehicles; ships; ANFIS; Matlab; PD controller; PD course control system; adaptive neuro-fuzzy inference system; batch learning scheme; least square algorithm; nonlinear ship model; oil tanker; ship autopilot design; Control system synthesis; Fuzzy systems; Least squares methods; Marine vehicles; Mathematical model; Nonlinear control systems; PD control; Petroleum; Robust control; Training data; ANFIS; Autopilot for ships; Simulation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2006 International Conference on
  • Conference_Location
    Dalian, China
  • Print_ISBN
    1-4244-0061-9
  • Type

    conf

  • DOI
    10.1109/ICMLC.2006.258664
  • Filename
    4028435