Fuzzy control for T-S fuzzy bilinear systems with time-delay in state and control input

Chen, Ta-Tau; Tsai, Shun-Hung; Tsai, Kun-Lin; Li, Ching-Lun

doi:10.1109/icsse.2011.5961926

Cited by 2 publications

(1 citation statement)

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“…By making use of a T-S fuzzy plant model, one can describe a nonlinear system as a weighted sum of some simple linear subsystems. The fuzzy model is represented by a family of fuzzy IF-THEN rules that describe local linear input/output relations of the system, and has been made a great of results on stability performance [4]- [10], filter design [11]- [15], robustness and tracking control [16] [17]. On the other hand, some novel T-S models have been proposed, for instance, T-S fuzzy bilinear model [18]- [20] and T-S fuzzy nonlinear model [21] were described for a class of nonlinear systems, respectively.…”

Section: Introductionmentioning

confidence: 99%

Robust feedback control for T-S stochastic fuzzy hyperbolic system

Zhang

2014

Proceedings of the 33rd Chinese Control Conference

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This paper concerns the problems of Takagi-Sugeno (T-S) stochastic fuzzy hyperbolic control for nonlinear systems with or without parameter uncertainties. The state feedback controller is designed by parallel distributed compensation (PDC) method, and some sufficient conditions are formulated via linear matrix inequalities (LMIs) such that the system is asymptotically stable. The above approach is also extended to the output feedback controller of T-S fuzzy hyperbolic system with parameter uncertainties, and the desired controller can be easily obtained by using the Matlab LMI toolbox. The main advantage of the proposed model is that it can achieve small control amplitude via "soft" constraint approach. Finally, two simulation examples are utilized to demonstrate the validity and effectiveness of the proposed approaches.

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