• DocumentCode
    2977501
  • Title

    Robust control based on neuro-fuzzy systems for a continuous stirred tank reactor

  • Author

    Liu, Shi-Rong ; Yu, Jin-shou

  • Author_Institution
    Fac. of Inf. Sci. & Technol., Ningbo Univ., China
  • Volume
    3
  • fYear
    2002
  • fDate
    2002
  • Firstpage
    1483
  • Abstract
    This paper studies the control problem of the concentration for a continuously stirred tank reactor (CSTR) in which parameter uncertainty and system disturbance are considered. A double control scheme, based on the PID control law and the internal model control strategy, is studied. Because the controller constructed by the neuro-fuzzy model is not very accurate and leads to control performance degradation, the double control scheme is proposed. The experiment study shows that the double control scheme adopted can extend effectively the controllable range and give good robust tracking control performances.
  • Keywords
    chemical variables control; fuzzy control; fuzzy neural nets; model reference adaptive control systems; neurocontrollers; process control; robust control; three-term control; uncertain systems; CSTR; IMC; PED control law; concentration control; continuous stirred tank reactor; controllable range; double control scheme; internal model control strategy; neuro-fuzzy systems; parameter uncertainty; robust control; robust tracking control performances; system disturbance; Automatic control; Continuous-stirred tank reactor; Control system synthesis; Control systems; Fuzzy neural networks; Fuzzy systems; Inverse problems; Neural networks; Robust control; Weight control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2002. Proceedings. 2002 International Conference on
  • Print_ISBN
    0-7803-7508-4
  • Type

    conf

  • DOI
    10.1109/ICMLC.2002.1167454
  • Filename
    1167454