• DocumentCode
    2337741
  • Title

    Model reference neural network control for boiler combustion system

  • Author

    Dong, Xiu-Cheng ; Wang, Hai-Bin ; Zhao, Xiao-Xiao

  • Author_Institution
    Sch. of Electr. & Inf. Eng., Xihua Univ., Sichuan, China
  • Volume
    8
  • fYear
    2005
  • fDate
    18-21 Aug. 2005
  • Firstpage
    4694
  • Abstract
    It is difficult to have good performance for chain boiler combustion control system due to large delay time, varying coal´s quality and steam load. A neural network identification method for nonlinear system´s delay time is discussed. Using the abrupt mutation resulted from the training error sum square of the real output and the expected output of the network, this method changes the input sample period of the neural network so that it can discriminate the delay time of the nonlinear model. Combining the discrimination of neural network system with long time delay and the control method based on model reference, it can be applied to control the nonlinear long delay time system with variable parameters or unknown delay time. Simulating with a 10t/h chain boiler model, the results show it has much better advantage of celerity and robustness.
  • Keywords
    boilers; combustion; delays; model reference adaptive control systems; neurocontrollers; nonlinear control systems; 10t/h chain boiler model; chain boiler combustion control system; delay time discrimination; model reference neural network control; mutation; neural network identification method; nonlinear system; Boilers; Combustion; Control systems; Delay effects; Delay lines; Delay systems; Equations; Neural networks; Nonlinear control systems; Process control; Neural network controller; chain boiler; delay time system; identification of delay time; reference model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
  • Conference_Location
    Guangzhou, China
  • Print_ISBN
    0-7803-9091-1
  • Type

    conf

  • DOI
    10.1109/ICMLC.2005.1527767
  • Filename
    1527767