• DocumentCode
    2571238
  • Title

    The unit Coordinated Control System based on the fuzzy neural network inverse control method

  • Author

    Wang, Qingli ; Jing, Yuanwei ; Wang, Lifu ; Kong, Zhi

  • Author_Institution
    Dept. of Inf. Eng., Shenyang Inst. of Eng., Shenyang
  • fYear
    2008
  • fDate
    2-4 July 2008
  • Firstpage
    5157
  • Lastpage
    5161
  • Abstract
    According to the performance of fossil fuel-fired units, that profound coupling nonlinearity and which made the mathematical model very difficult. Inverse method control for the design of MIMO system of unit coordinated control system is suggested. the realization of the inverse system up is not adopt traditional meaning is of the obvious analyze, but adopt the dynamic neural network, the inverse system that construct to non-analytical model, combine the neural network inverse system and the linear PID control to construct closed loop system, apply in the boiler-turbine coordinated control system. Through simulation testing, this kind of new control method not only has the satisfied control performance, but also has the stronger robustness and interference resistance, improve coordinated control system of fossil fuel-fired units altering operating mode of adaptability and the control quality.
  • Keywords
    MIMO systems; boilers; closed loop systems; control system synthesis; fossil fuels; fuzzy control; neurocontrollers; nonlinear control systems; three-term control; MIMO system; boiler-turbine coordinated control system; closed loop system; fossil fuel-fired units; fuzzy neural network inverse control method; linear PID control; unit coordinated control system; Control system synthesis; Control systems; Couplings; Fuzzy control; Fuzzy neural networks; Inverse problems; MIMO; Mathematical model; Neural networks; Nonlinear dynamical systems; Decoupling Control instruction; Fuzzy Neural Network control; Inverse System; Single Generating Units;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control and Decision Conference, 2008. CCDC 2008. Chinese
  • Conference_Location
    Yantai, Shandong
  • Print_ISBN
    978-1-4244-1733-9
  • Electronic_ISBN
    978-1-4244-1734-6
  • Type

    conf

  • DOI
    10.1109/CCDC.2008.4598313
  • Filename
    4598313