• DocumentCode
    1983420
  • Title

    Application of RBF Neural Network PID Controller in the Rectification Column Temperature Control System

  • Author

    Yan Zhang ; Chaoying Liu ; Xueling Song ; Zhifei Yan

  • Author_Institution
    Inst. of Electr. Eng., Hebei Univ. of Sci. & Technol., Shijiazhuang, China
  • Volume
    2
  • fYear
    2013
  • fDate
    28-29 Oct. 2013
  • Firstpage
    72
  • Lastpage
    75
  • Abstract
    The temperature control of the rectification column is an important part of distillation process control system. For the time-delay and parameter time-varying characteristics in rectification column temperature control system, it puts forward neural network self-tuning PID controller method which combines the advantages of traditional PID control and neural network radial basis function (RBF). From the simulation experiment results it shows that RBF neural network PID controller gets much better control effect, and it verifies the effectiveness of the proposed method.
  • Keywords
    adaptive control; delay systems; distillation; distillation equipment; neurocontrollers; process control; radial basis function networks; rectification; self-adjusting systems; temperature control; three-term control; time-varying systems; RBF neural network PID controller; control effect; distillation process control system; forward neural network self-tuning PID controller method; neural network radial basis function; parameter time-varying characteristics; rectification column temperature control system; simulation experiment; time-delay; Artificial neural networks; Biological neural networks; Educational institutions; Object recognition; PD control; Temperature control; Neural network PID controller; RBF neural network; Rectification column; Simulation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Design (ISCID), 2013 Sixth International Symposium on
  • Conference_Location
    Hangzhou
  • Type

    conf

  • DOI
    10.1109/ISCID.2013.132
  • Filename
    6804831