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
Link To Document :
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