An observer-based robust H∞ controller design for uncertain Takagi–Sugeno fuzzy systems with unknown premise variables using particle swarm optimisation

2020 ◽  
Vol 51 (14) ◽  
pp. 2563-2581 ◽  
Author(s):  
Hakim Achour ◽  
Djamel Boukhetala ◽  
Hilal Labdelaoui
Keyword(s):  
Fuzzy Systems ◽  
Controller Design ◽  
Particle Swarm ◽  
Particle Swarm Optimisation ◽  
Takagi Sugeno

scholarly journals Improvements on PDC Controller Design for Takagi-Sugeno Fuzzy Systems with State Time-Varying Delays

2016 ◽  
Vol 49 (5) ◽  
pp. 200-205 ◽  
Author(s):  
Fayçal Bourahala ◽  
Kevin Guelton ◽  
Farid Khaber ◽  
Noureddine Manamanni
Keyword(s):  
Fuzzy Systems ◽  
Controller Design ◽  
Time Varying ◽  
State Time ◽  
Takagi Sugeno

Rule Reduction and Robust Control of Generalized Takagi-Sugeno Fuzzy Systems

2000 ◽  
Vol 4 (5) ◽  
pp. 373-379 ◽  
Author(s):  
Tadanari Taniguchi ◽  
◽  
Kazuo Tanaka ◽  
Keyword(s):  
Robust Control ◽  
Model Reduction ◽  
Fuzzy System ◽  
Fuzzy Systems ◽  
Controller Design ◽  
Main Idea ◽  
Linear Matrix ◽  
Robust Controller ◽  
Reduction Error ◽  
Takagi Sugeno

This paper presents model reduction and robust control using a generalized form of Takagi-Sugeno fuzzy systems. We first define a generalized form of TakagiSugeno fuzzy systems. The generalized form has a decomposed structure for each element of <I>Ai</I> and <I>Bi</I> matrices in consequent parts. The key feature of this structure is that it is suitable for reducing the number of rules. Conditions to reduce the number of rules are represented in terms of linear matrix inequality (LMIs). The main idea is to find a structure of if-then rules of the reduced model that agrees well with dynamics of the original model. Furthermore, we estimate the lower bound of the norm of model uncertainty of the Takagi-Sugeno fuzzy system that can cover the reduction error. Finally, we present an example of model reduction and robust control for a nonlinear system. In this example, we achieve a robust controller design to compensate for the uncertainly of the Takagi-Sugeno fuzzy system.

scholarly journals Training High-Order Takagi-Sugeno Fuzzy Systems Using Batch Least Squares and Particle Swarm Optimization

2019 ◽  
Vol 22 (1) ◽  
pp. 22-34 ◽  
Author(s):  
Krzysztof Wiktorowicz ◽  
Tomasz Krzeszowski
Keyword(s):  
Particle Swarm Optimization ◽  
Least Squares ◽  
Fuzzy Sets ◽  
Fuzzy Systems ◽  
Piecewise Linear ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Takagi Sugeno

AbstractThis paper proposes two methods for training Takagi–Sugeno (T-S) fuzzy systems using batch least squares (BLS) and particle swarm optimization (PSO). The T-S system is considered with triangular and Gaussian membership functions in the antecedents and higher-order polynomials in the consequents of fuzzy rules. In the first method, the BLS determines the polynomials in a system in which the fuzzy sets are known. In the second method, the PSO algorithm determines the fuzzy sets, whereas the BLS determines the polynomials. In this paper, the ridge regression is used to stabilize the solution when the problem is close to the singularity. Thanks to this, the proposed methods can be applied when the number of observations is less than the number of predictors. Moreover, the leave-one-out cross-validation is used to avoid overfitting and this way to choose the structure of a fuzzy model. A method of obtaining piecewise linear regression by means of the zero-order T-S system is also presented.

Fault Estimation and Fault-Tolerant Control Via a Novel Proportional–Integral Observer in Singular Takagi-Sugeno Fuzzy Systems

2019 ◽  
Vol 141 (9) ◽  
Author(s):  
Min Li ◽  
Ming Liu ◽  
Yingchun Zhang ◽  
Zhuo Chen
Keyword(s):  
Fuzzy Systems ◽  
Controller Design ◽  
Fault Tolerant ◽  
Sufficient Conditions ◽  
Fault Estimation ◽  
Linear Matrix ◽  
Closed Loop System ◽  
Proportional Integral ◽  
System States ◽  
Takagi Sugeno

This paper deals with the fault observer and fault-tolerant controller design for singular Takagi–Sugeno (T–S) fuzzy systems subject to actuator faults. First, a novel proportional-integral observer is constructed to estimate the system states and faults. Sufficient conditions for the existence of the proposed observer are given in linear matrix inequality (LMI) terms. Furthermore, based on the state and fault estimation (FE), a fault-tolerant controller (FTC) is designed to effectively accommodate the influence of fault upon state and ensure that the closed-loop system is stable. Finally, a numerical example is given to show the effectiveness of the presented method.

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