• DocumentCode
    2850019
  • Title

    Seam Tracking Based on Fuzzy-Gaussian Neural Network for Mobile Welding Robot

  • Author

    Li Kai ; Zhang Ting ; Zhang Libin ; Xiao Shumei

  • Author_Institution
    Dept. of Mech. Eng., Hebei Univ. of Technol., Tianjin, China
  • fYear
    2009
  • fDate
    1-3 Nov. 2009
  • Firstpage
    261
  • Lastpage
    264
  • Abstract
    To solve the seam tracking problem of the welding mobile robot, adaptive fuzzy controller and fuzzy-Gaussian neural network (FGNN) controller are designed to complete coordinately controlling of cross-slider and wheels. The fuzzy-neural control algorithm was described by applying a Gaussian function as an activation function, taking lateral slider position and the error between the robot moving direction and the seam direction as the inputs signals, and the adjusted angle for welding torch as the output, a specialized learning architecture was used so that membership function would be tuned in real time by applying the FGNN controller. The simulation results on MATLAB show that the proposed controller has excellent tracing accuracy (within 0.5mm) and can satisfy the requirement of practical welding project.
  • Keywords
    Gaussian processes; adaptive control; fuzzy control; learning systems; mobile robots; neurocontrollers; robotic welding; MATLAB; adaptive fuzzy controller; fuzzy-Gaussian neural network controller; lateral slider position; mobile welding robot; seam tracking; specialized learning architecture; welding torch; Adaptive control; Fuzzy control; Fuzzy neural networks; Mobile robots; Neural networks; Programmable control; Robot control; Robot kinematics; Welding; Wheels; Mobile welding robot; adaptive fuzzy control; coordinate control; fuzzy-gaussian neural network; seam tracking;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Networks and Intelligent Systems, 2009. ICINIS '09. Second International Conference on
  • Conference_Location
    Tianjin
  • Print_ISBN
    978-1-4244-5557-7
  • Electronic_ISBN
    978-0-7695-3852-5
  • Type

    conf

  • DOI
    10.1109/ICINIS.2009.73
  • Filename
    5365326