• DocumentCode
    175481
  • Title

    Reinforcement learning control for gas collector pressure of coke ovens

  • Author

    Qin Bin ; Dai Leqiang ; Li Pengcheng ; Zhu Wangli ; Wang Xin

  • Author_Institution
    Sch. of Electr. & Inf. Eng., Hunan Univ. of Technol., Zhuzhou, China
  • fYear
    2014
  • fDate
    May 31 2014-June 2 2014
  • Firstpage
    414
  • Lastpage
    417
  • Abstract
    Due to nonlinear and strong coupling of coke ovens, a distributed reinforcement learning control algorithm based on a radial basis function (RBF) for gas collector pressure control was proposed, in which the discrete space was dealt with by RBF and the critic-actor method was used to update the parameters of search network and evaluation network of the controller. Moreover, the control strategies of the controller were optimized by trial and error in the strong interference environment. The simulation of gas collector pressure control of coke ovens has shown that the algorithm can effectively solve the coupling problem, and has better stability.
  • Keywords
    coke; control engineering computing; fuel processing industries; learning (artificial intelligence); nonlinear control systems; ovens; pressure control; radial basis function networks; stability; RBF; coke ovens; critic-actor method; distributed reinforcement learning control; gas collector pressure control; nonlinear coupling; radial basis function; stability; strong coupling; Couplings; Educational institutions; Interference; Learning (artificial intelligence); Ovens; Pressure control; Valves; coke oven; coupling control; gas collector pressure; reinforcement learning;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Control and Decision Conference (2014 CCDC), The 26th Chinese
  • Conference_Location
    Changsha
  • Print_ISBN
    978-1-4799-3707-3
  • Type

    conf

  • DOI
    10.1109/CCDC.2014.6852183
  • Filename
    6852183