System Modeling Using Type-2 Takagi-Sugeno Fuzzy Systems

2017 ◽  
pp. 59-79
Author(s):  
Rómulo Antão ◽  
Alexandre Mota ◽  
Rui Escadas Martins ◽  
José Tenreiro Machado
Keyword(s):  
Fuzzy Systems ◽  
System Modeling ◽  
Takagi Sugeno

Stable fuzzy controllers via LMI approach for non-linear systems described by type-2 T–S fuzzy model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Himanshukumar R. Patel ◽  
Vipul A. Shah
Keyword(s):  
Fuzzy Systems ◽  
Fuzzy Model ◽  
System Stability ◽  
Stability Conditions ◽  
Lmi Approach ◽  
Fuzzy Controllers ◽  
Content Type ◽  
Control Approach ◽  
Takagi Sugeno

PurposeThe purpose of this paper is to stabilize the type-2 Takagi–Sugeno (T–S) fuzzy systems with the sufficient and guaranteed stability conditions. The given conditions efficaciously handle parameter uncertainties by the upper and lower membership functions of the type-2 fuzzy sets (FSs).Design/methodology/approachThis paper reports on a relevant study of stable fuzzy controllers and type-2 T–S fuzzy systems and reported that the synthesis of controller for nonlinear systems described by the type-2 T–S fuzzy model is a key problem and it can be resolve to convex problems via linear matrix inequalities (LMIs).FindingsThe multigain fuzzy controllers are established to improve the solvability of the stability conditions, and the authors design multigain fuzzy controllers which have extensive information of upper and lower membership grades. Consequently, the authors derive the traditional stability condition in terms of LMIs. One simulation examples illustrate the effectiveness and robustness of the derived stabilization conditions.Originality/valueThe uncertain MIMO nonlinear system described by Type-2 Takagi-Sugeno (T-S) fuzzy model, and successively LMI approach used to determine the system stability conditions. The proposed control approach will give superior fault-tolerant control permanence under the actuator fault [partial loss of effectiveness (LOE)]. Also the controller robust against the unmeasurable process disturbances. Additionally, the statistical z-test are carried out to validate the proposed control approach against the control approach proposed by Himanshukumar and Vipul (2019a).

scholarly journals Adaptive Proportional Integral Observer Design for Interval Type 2 Takagi–Sugeno Fuzzy Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Atef Khedher ◽  
Ilyes Elleuch ◽  
Kamal BenOthman
Keyword(s):  
Fuzzy Systems ◽  
Observer Design ◽  
Adaptive Observer ◽  
Fault Estimation ◽  
Sensor Faults ◽  
Proportional Integral ◽  
Augmented State ◽  
Interval Type ◽  
Takagi Sugeno

In this paper, the problem of fault estimation in systems described by Takagi–Sugeno fuzzy systems is studied. A proportional integral observer is conceived in order to reconstruct state and faults which can affect the studied system. Proportional integral observer can easily estimate actuator faults which are assimilated to be as unknown inputs. In order to estimate actuator and sensor faults, a mathematical transformation is used to conceive an augmented system, in which the initial sensor fault appears as an unknown input. Considering the augmented state, it is possible to conceive an adaptive observer which is able to estimate the whole state and faults. The noise effect on the state and fault estimation is also minimized in this study, which provides some robustness properties to the proposed observer. The proportional integral observer is conceived for nonlinear systems described by Takagi–Sugeno fuzzy models.

scholarly journals Admissibilisation of singular interval type-2 Takagi–Sugeno fuzzy systems with time delay

2020 ◽  
Vol 14 (8) ◽  
pp. 1022-1032 ◽  
Author(s):  
Zhiguang Feng ◽  
Huayang Zhang ◽  
Haiping Du ◽  
Zhengyi Jiang
Keyword(s):  
Time Delay ◽  
Fuzzy Systems ◽  
Interval Type ◽  
Takagi Sugeno

Model Predictive Control Using Type-2 Takagi-Sugeno Fuzzy Systems

2017 ◽  
pp. 81-110
Author(s):  
Rómulo Antão ◽  
Alexandre Mota ◽  
Rui Escadas Martins ◽  
José Tenreiro Machado
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Fuzzy Systems ◽  
Takagi Sugeno

scholarly journals Hybrid-Learning Type-2 Takagi–Sugeno–Kang Fuzzy Systems for Temperature Estimation in Hot-Rolling

Metals ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 758
Author(s):  
José Ángel Barrios ◽  
Gerardo Maximiliano Méndez ◽  
Alberto Cavazos
Keyword(s):  
Fuzzy Systems ◽  
Real Life ◽  
Hybrid Learning ◽  
Temperature Estimation ◽  
Box Models ◽  
Finishing Mill ◽  
Improved Performance ◽  
Set Up ◽  
Takagi Sugeno

Entry temperature estimation is a major concern for finishing mill set-up in hot strip mills. Variations in the incoming bar conditions, frequent product changes and measurement uncertainties may cause erroneous estimation, and hence, an incorrect mill set-up causing a faulty bar head-end. In earlier works, several varieties of neuro-fuzzy systems have been tested due to their adaptation capabilities. In order to test the combination of the simplicity offered by Takagi–Sugeno–Kang systems (also known as Sugeno systems) and the modeling power of type-2 fuzzy, in this work, hybrid-learning type-2 Sugeno fuzzy systems are evaluated and compared with the results presented earlier. Systems with both empirically and fuzzy c-means-generated rules as well as purely fuzzy systems and grey-box models are tested. Experimental data were collected from a real-life mill; datasets for rule-generation, training, and validation were randomly drawn. Two of the grey-box models presented here reach 100% of bars with 20 °C or less prediction error, while two of the purely fuzzy systems improved performance with respect to purely fuzzy systems presented elsewhere, however it was only a slight improvement.

scholarly journals Improved Approach to Robust Control for Type-2 T-S Fuzzy Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Bum Yong Park ◽  
JaeWook Shin
Keyword(s):  
Fuzzy Systems ◽  
Linear Matrix ◽  
Stability Conditions ◽  
Parameter Uncertainties ◽  
Comprehensive Information ◽  
Stabilization Condition ◽  
Feasible Solutions ◽  
The Stability ◽  
Takagi Sugeno

This paper is concerned with the robust stability conditions to stabilize the type 2 Takagi-Sugeno (T-S) fuzzy systems. The conditions effectively handle parameter uncertainties using lower and upper membership functions. To improve the solvability of the stability conditions, we establish a multigain controller with comprehensive information of the lower and upper membership grades. In addition, a well-organized relaxation technique is proposed to fully exploit relationship among fuzzy weighting functions and their lower and upper membership grades, which enlarges a set of feasible solutions. Therefore, we derive a less conservative stabilization condition in terms of linear matrix inequalities (LMIs) than those in the literature. Two simulation examples illustrate the effectiveness and robustness of the derived stabilization conditions.

Model reduction for interval type-2 Takagi–Sugeno fuzzy systems

Automatica ◽  
2015 ◽  
Vol 61 ◽  
pp. 308-314 ◽  
Author(s):  
Hongyi Li ◽  
Shen Yin ◽  
Yingnan Pan ◽  
Hak-Keung Lam
Keyword(s):  
Model Reduction ◽  
Fuzzy Systems ◽  
Interval Type ◽  
Takagi Sugeno
Sign in / Sign up

Export Citation Format

Share Document