Takagi-Sugeno fuzzy models within orthonormal basis function framework and their application to process control

2003 ◽  
Author(s):  
R.J.G.B. Campello ◽  
W.C. Amaral
Keyword(s):  
Process Control ◽  
Basis Function ◽  
Orthonormal Basis ◽  
Fuzzy Models ◽  
Takagi Sugeno

Takagi–Sugeno Fuzzy Models in the Framework of Orthonormal Basis Functions

2013 ◽  
Vol 43 (3) ◽  
pp. 858-870 ◽  
Author(s):  
J. B. Machado ◽  
R. J. G. B. Campello ◽  
W. C. Amaral
Keyword(s):  
Orthonormal Basis ◽  
Basis Functions ◽  
Fuzzy Models ◽  
Takagi Sugeno

A modified gradient learning algorithm with smoothing L1/2 regularization for Takagi–Sugeno fuzzy models

2014 ◽  
Vol 138 ◽  
pp. 229-237 ◽  
Author(s):  
Yan Liu ◽  
Wei Wu ◽  
Qinwei Fan ◽  
Dakun Yang ◽  
Jian Wang
Keyword(s):  
Learning Algorithm ◽  
Fuzzy Models ◽  
Gradient Learning ◽  
Takagi Sugeno

scholarly journals A Control Approach Based on Observers of State and Uncertainty for a Class of Takagi–Sugeno Fuzzy Models

2021 ◽  
Vol 25 (3) ◽  
pp. 317-325
Author(s):  
Jun Zhao ◽  
Hugang Han ◽  
◽  
Keyword(s):  
Control System ◽  
Control Systems ◽  
Fuzzy Model ◽  
State Observer ◽  
The State ◽  
State Information ◽  
Control Approach ◽  
Fuzzy Models ◽  
Takagi Sugeno ◽  
Stable Control

Although the Takagi–Sugeno fuzzy model is effective for representing the dynamics of a plant to be controlled, two main questions arise when using it just as other models: 1) how to deal with the gap, which is referred to as uncertainty in this study, between the model and the concerned plant, and how to estimate the state information when it cannot be obtained directly, especially with the existence of uncertainty; 2) how to design a controller that guarantees a stable control system where only the estimated state is available and an uncertainty exists. While the existing studies cannot effectively observe the state and the resulting control systems can only be managed to be uniformly stable, this study first presents a state observer capable of precisely estimating the state regardless of the existence of uncertainty. Then, based on the state observer, an uncertainty observer is derived, which can track the trajectory of uncertainty whenever it occurs in a real system. Finally, a controller based on both observers is presented, which guarantees the asymptotic stability of the resulting control system.

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