• DocumentCode
    3101503
  • Title

    H output feedback control of discrete-time stochastic T-S fuzzy models with state-dependent noise

  • Author

    Lin, Hsuan-Heng ; Lee, Bore-kuen ; Wu, Chein-Fong

  • Author_Institution
    Dept. of Electr. Eng., Chung Hua Univ., Hsinchu, Taiwan
  • Volume
    6
  • fYear
    2009
  • fDate
    12-15 July 2009
  • Firstpage
    3264
  • Lastpage
    3269
  • Abstract
    In this paper, Hinfin dynamic output feedback control for discrete-time nonlinear stochastic T-S fuzzy model with state-dependent noise is attacked. We consider the fuzzy T-S models has has stochastic uncertainties, i.e., state-dependent noise, in the system matrix, input matrix, and output matrix. First, when the premise variables in the fuzzy plant model are available, an Hinfin fuzzy dynamic output feedback controller, which uses the same premise variables as the T-S fuzzy model, is proposed for regulation of the controlled system to meet the Hinfin control performance specification. Next, when the premise variables for building the fuzzy plant model are not available, a fuzzy Hinfin observer-based state feedback controller, in which the premise variables are the estimated version of the premise variables in the T-S fuzzy model, is proposed. For the two cases, we conduct sufficient conditions described by linear matrix inequalities (LMI) to ensure stability of the closed-loop system. Performance of the proposed fuzzy controller is verified by simulation study.
  • Keywords
    Hinfin control; closed loop systems; discrete time systems; fuzzy control; linear matrix inequalities; nonlinear control systems; observers; stability; state feedback; stochastic systems; uncertain systems; Hinfin control performance specification; Hinfin dynamic output feedback control; closed-loop system stability; controlled system regulation; discrete-time nonlinear stochastic T-S fuzzy model; fuzzy Hinfin observer; fuzzy plant model; linear matrix inequalities; state feedback controller; state-dependent noise; stochastic uncertainties; Control system synthesis; Fuzzy control; Fuzzy systems; Nonlinear dynamical systems; Output feedback; State estimation; State feedback; Stochastic resonance; Stochastic systems; Uncertainty; H; Output feedback; Stochastic T-S fuzzy model; control;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2009 International Conference on
  • Conference_Location
    Baoding
  • Print_ISBN
    978-1-4244-3702-3
  • Electronic_ISBN
    978-1-4244-3703-0
  • Type

    conf

  • DOI
    10.1109/ICMLC.2009.5212736
  • Filename
    5212736