Robust Stabilization of Takagi-Sugeno Fuzzy Systems with Parametric Uncertainties Using Fuzzy Region Concept

Author(s):  
Zhicheng Ji ◽  
Yinghuan Zhou ◽  
Yanxia Shen
2011 ◽  
Vol 181 (23) ◽  
pp. 5278-5290 ◽  
Author(s):  
Xiaohong Zhang ◽  
Chengliang Wang ◽  
Dong Li ◽  
Xiaolong Zhou ◽  
Dan Yang

Author(s):  
Jun Yoneyama ◽  

This paper is concerned with robust output feedback stabilization with guaranteed cost of uncertain Takagi-Sugeno fuzzy systems with immeasurable premise variables. When we consider Takagi-Sugeno fuzzy systems, the selection of premise variables plays an important role. If the premise variable is the state of the system, then a fuzzy system describes a wide class of nonlinear systems. However, the state is not measurable in the output feedback control problem. In this case, a control design based on parallel distributed compensation is impossible because a controller depends on immeasurable premise variables. In this paper, we consider the robust output feedback stabilization problem with guaranteed cost where the premise variable is not measurable. We formulate this problem as a robust stabilization of uncertain linear systems. Numerical examples are given to illustrate our theory.


2020 ◽  
Vol 9 (3) ◽  
pp. 63-99
Author(s):  
Iqbal Ahammed A.K. ◽  
Mohammed Fazle Azeem

Most of the systems in the industry contain extreme non-linearity and uncertainties, which are hard to design and control utilizing general nonlinear systems. To conquer this sort of troubles, different plans have been produced in the most recent two decades, among which a popular methodology is Takagi-Sugeno fuzzy control. In this article, we present robust stabilization and control of Takagi-Sugeno (T-S) fuzzy systems with parameter uncertainties and disturbances. Initially, Takagi and Sugeno (TS) fuzzy model is used to represent a nonlinear system. Based on this T-S fuzzy model, fuzzy controller design schemes for state feedback and output feedback is also developed. Then, necessary conditions are derived for robust stabilization in the intelligence of Lyapunov asymptotic stability and are expressed in the arrangement of linear matrix inequalities (LMIs). The proposed system is implemented in the working platform of MATLAB and the simulation results are provided to illustrate the effectiveness of the proposed methods.


2001 ◽  
Vol 32 (7) ◽  
pp. 915-924 ◽  
Author(s):  
Jun Yoneyama ◽  
Masahiro Nishikawa ◽  
Hitoshi Katayama ◽  
Akira Ichikawa
Keyword(s):  

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