• DocumentCode
    1688394
  • Title

    Decoupling control of forging machine based on PID neural network

  • Author

    Zhang, Dapeng ; Zhai, Wenpeng ; Wu, Aiguo ; Du, Chunyan

  • Author_Institution
    Sch. of Electr. Eng. & Autom., Tianjin Univ., Tianjin, China
  • fYear
    2010
  • Firstpage
    1858
  • Lastpage
    1861
  • Abstract
    A PID neural network is proposed to solve the decoupling problem of multi-cylinder forging machine. Based on analyzing forging machine´s model the PID neural network is determined as 10-15-5 structure. 10 input- neurons connect the given value and the measure value of left front cylinder, left posterior cylinder, right front cylinder, right posterior cylinder and main cylinder.15 neurons of hidden layer respond 5 groups of P-neuron, I-neuron and D-neuron. And 5 output-neurons connect 5 circuit controllers. The learning process uses BP algorithm. The simulation shows this approach is effective.
  • Keywords
    backpropagation; forging; neurocontrollers; three-term control; BP algorithm; PID neural network; decoupling control; learning process; multicylinder forging machine; posterior cylinder; Artificial neural networks; Cavity resonators; Control systems; Mathematical model; Neurons; Petroleum; Valves; Decoupling; Forging machine; PID neural network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Automation (WCICA), 2010 8th World Congress on
  • Conference_Location
    Jinan
  • Print_ISBN
    978-1-4244-6712-9
  • Type

    conf

  • DOI
    10.1109/WCICA.2010.5554480
  • Filename
    5554480