Takagi–Sugeno Fuzzy Model Based Fault Estimation and Signal Compensation With Application to Wind Turbines

2017 ◽  
Vol 64 (7) ◽  
pp. 5678-5689 ◽  
Author(s):  
Xiaoxu Liu ◽  
Zhiwei Gao ◽  
Michael Z. Q. Chen
Keyword(s):  
Wind Turbines ◽  
Fuzzy Model ◽  
Fault Estimation ◽  
Model Based ◽  
Takagi Sugeno

Fault-tolerant controller design with fault estimation capability for a class of nonlinear systems using generalized Takagi-Sugeno fuzzy model

2019 ◽  
Vol 41 (15) ◽  
pp. 4218-4229 ◽  
Author(s):  
Alireza Navarbaf ◽  
Mohammad Javad Khosrowjerdi
Keyword(s):  
Nonlinear Systems ◽  
Controller Design ◽  
Fault Tolerant ◽  
Fuzzy Model ◽  
Fault Estimation ◽  
Linear Matrix ◽  
Wide Range ◽  
Satisfy Lipschitz Condition ◽  
Unknown Disturbances ◽  
Takagi Sugeno

In this paper, a new design approach to construct a fault-tolerant controller (FTC) with fault estimation capability is proposed using a generalized Takagi-Sugeno (T-S) fuzzy model for a class of nonlinear systems in the presence of actuator faults and unknown disturbances. The generalized T-S fuzzy model consists of some local models with multiplicative nonlinear terms that satisfy Lipschitz condition. Besides covering a very wide range of nonlinear systems with a smaller number of local rules in comparison with the conventional T-S fuzzy model and hence having less computational burden, the existence of the multiplicative nonlinear term solves the uncontrollability issues that the other generalized T-S fuzzy models with additive nonlinear terms dealt with. A state/fault observer designed for the considered generalized T-S fuzzy model and then, a dynamic FTC law based on the estimated fault information is proposed and sufficient design conditions are given in terms of linear matrix inequalities (LMIs). It can be shown that the number of LMIs are less than that of previously proposed methods and then feasibility of our method is more likely. The effectiveness of the proposed FTC approach is verified using a nonlinear mass-spring-damper system.

Evolving Takagi–Sugeno fuzzy model based on switching to neighboring models

2013 ◽  
Vol 13 (2) ◽  
pp. 939-946 ◽  
Author(s):  
Ahmad Kalhor ◽  
Babak N. Araabi ◽  
Caro Lucas
Keyword(s):  
Fuzzy Model ◽  
Model Based ◽  
Takagi Sugeno
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