Neuro-fuzzy systems of Mamdani, logical and Takagi-Sugeno type

2008 ◽  
pp. 371-448
Keyword(s):  
Fuzzy Systems ◽  
Neuro Fuzzy ◽  
Takagi Sugeno

Exploiting the Functional Training Approach in Takagi-Sugeno Neuro-fuzzy Systems

2013 ◽  
pp. 543-559 ◽  
Author(s):  
Cristiano L. Cabrita ◽  
António E. Ruano ◽  
Pedro M. Ferreira ◽  
László T. Kóczy
Keyword(s):  
Fuzzy Systems ◽  
Functional Training ◽  
Neuro Fuzzy ◽  
Training Approach ◽  
Takagi Sugeno

H∞ control for Takagi–Sugeno fuzzy systems

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

Modeling of Self-Induced Vibrations that Occur During the Machining Process of Casting Patterns with the Use of The Fuzzy-Neural Networks Method

2013 ◽  
Vol 58 (3) ◽  
pp. 871-875
Author(s):  
A. Herberg
Keyword(s):  
Neural Networks ◽  
Control System ◽  
Network Structure ◽  
Fuzzy Systems ◽  
Fuzzy Neural Networks ◽  
Cnc Machining ◽  
Machining Process ◽  
Modeling Process ◽  
Fuzzy Neural ◽  
Takagi Sugeno

Abstract This article outlines a methodology of modeling self-induced vibrations that occur in the course of machining of metal objects, i.e. when shaping casting patterns on CNC machining centers. The modeling process presented here is based on an algorithm that makes use of local model fuzzy-neural networks. The algorithm falls back on the advantages of fuzzy systems with Takagi-Sugeno-Kanga (TSK) consequences and neural networks with auxiliary modules that help optimize and shorten the time needed to identify the best possible network structure. The modeling of self-induced vibrations allows analyzing how the vibrations come into being. This in turn makes it possible to develop effective ways of eliminating these vibrations and, ultimately, designing a practical control system that would dispose of the vibrations altogether.

NEURO-FUZZY SYSTEMS APPLIED ON A FIXED-WING AIRCRAFT CONTROL

2019 ◽  
Author(s):  
Renata Bernardes ◽  
Bruno Luiz Pereira ◽  
Felipe Machini Malachias Marques ◽  
Roberto Mendes Finzi Neto
Keyword(s):  
Fuzzy Systems ◽  
Aircraft Control ◽  
Neuro Fuzzy

Observer-based proportional derivative fuzzy control for singular Takagi-Sugeno fuzzy systems

2021 ◽  
Author(s):  
Cheung-Chieh Ku ◽  
Wen-Jer Chang ◽  
Ming-Hsuan Tsai ◽  
Yi-Chen Lee
Keyword(s):  
Fuzzy Control ◽  
Fuzzy Systems ◽  
Takagi Sugeno

LMI-based control synthesis of constrained Takagi–Sugeno fuzzy systems subject toL2orL∞ disturbances

2016 ◽  
Vol 207 ◽  
pp. 793-804 ◽  
Author(s):  
Anh-Tu Nguyen ◽  
Thomas Laurain ◽  
Reinaldo Palhares ◽  
Jimmy Lauber ◽  
Chouki Sentouh ◽  
...  
Keyword(s):  
Fuzzy Systems ◽  
Control Synthesis ◽  
Takagi Sugeno

Robust Tracking Control for Takagi–Sugeno Fuzzy Systems With Unmeasurable Premise Variables: Application to Tank System

2014 ◽  
Vol 136 (4) ◽  
Author(s):  
H. Ghorbel ◽  
A. El Hajjaji ◽  
M. Souissi ◽  
M. Chaabane
Keyword(s):  
Tracking Control ◽  
Fuzzy Systems ◽  
Design Procedure ◽  
Control Design ◽  
Linear Matrix ◽  
Robust Tracking Control ◽  
Fuzzy Observer ◽  
Tank System ◽  
System States ◽  
Takagi Sugeno

In this paper, a robust fuzzy observer-based tracking controller for continuous-time nonlinear systems presented by Takagi–Sugeno (TS) models with unmeasurable premise variables, is synthesized. Using the H∞ norm and Lyapunov approach, the control design for TS fuzzy systems with both unmeasurable premises and system states is developed to guarantee tracking performance of closed loop systems. Sufficient relaxed conditions for synthesis of the fuzzy observer and the fuzzy control are driven in terms of linear matrix inequalities (LMIs) constraints. The proposed method allows simplifying the design procedure and gives the observer and controller gains in only one step. Numerical simulation on a two tank system is provided to illustrate the tracking control design procedure and to confirm the efficiency of the proposed method.

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