scholarly journals Takagi-Sugeno fuzzy model identification for turbofan aero-engines with guaranteed stability

2018 ◽  
Vol 31 (6) ◽  
pp. 1206-1214 ◽  
Author(s):  
Ruichao LI ◽  
Yingqing GUO ◽  
Sing Kiong NGUANG ◽  
Yifeng CHEN
Keyword(s):  
Model Identification ◽  
Fuzzy Model ◽  
Guaranteed Stability ◽  
Aero Engines ◽  
Takagi Sugeno

A new imputation-based incomplete data-driven fuzzy modeling for accuracy improvement in ubiquitous computing applications

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sonia Goel ◽  
Meena Tushir
Keyword(s):  
Parameter Estimation ◽  
Missing Data ◽  
Incomplete Data ◽  
Model Identification ◽  
Fuzzy Model ◽  
New Method ◽  
Content Type ◽  
Ubiquitous Environment ◽  
Sugeno Model ◽  
Takagi Sugeno

Purpose In real-world decision-making, high accuracy data analysis is essential in a ubiquitous environment. However, we encounter missing data while collecting user-related data information because of various privacy concerns on account of a user. This paper aims to deal with incomplete data for fuzzy model identification, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features. Design/methodology/approach In this work, authors proposed a three-fold approach for fuzzy model identification in which imputation-based linear interpolation technique is used to estimate missing features of the data, and then fuzzy c-means clustering is used for determining optimal number of rules and for the determination of parameters of membership functions of the fuzzy model. Finally, the optimization of the all antecedent and consequent parameters along with the width of the antecedent (Gaussian) membership function is done by gradient descent algorithm based on the minimization of root mean square error. Findings The proposed method is tested on two well-known simulation examples as well as on a real data set, and the performance is compared with some traditional methods. The result analysis and statistical analysis show that the proposed model has achieved a considerable improvement in accuracy in the presence of varying degree of data incompleteness. Originality/value The proposed method works well for fuzzy model identification method, a new method of parameter estimation of a Takagi–Sugeno model in the presence of missing features with varying degree of missing data as compared to some well-known methods.

Stability Analysis of QFT Controllers Designed for Hydraulic Actuators Using Takagi-Sugeno Fuzzy Modeling Approach

2013 ◽  
Author(s):  
Masoumeh Esfandiari ◽  
Nariman Sepehri
Keyword(s):  
Stability Analysis ◽  
Linear Models ◽  
Fuzzy Model ◽  
Hydraulic Actuators ◽  
Local Linear ◽  
Guaranteed Stability ◽  
Wide Range ◽  
Position Controller ◽  
The Stability ◽  
Takagi Sugeno

Although, robust controllers that have been designed for hydraulic actuators based on quantitative feedback theory (QFT) have shown satisfactory performance, their stability is limited to certain set of inputs-outputs. This paper explores, for the first time, the stability of a QFT controller using stability theorem of Takagi-Sugeno (T-S) fuzzy systems. To do this, first the hydraulic closed-loop system is represented by a T-S fuzzy model that is formed through a nonlinear combination of some local linear models. Next, the stability of the resulting T-S fuzzy system is analyzed simply by stability analysis of its local linear models. This approach is used to study the stability of a QFT position controller previously developed for hydraulic actuators. Results show guaranteed stability of the QFT controller over a wide range of operation and in the presence of parametric uncertainties.

scholarly journals Robust Kernel Clustering Algorithm for Nonlinear System Identification

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Mohamed Bouzbida ◽  
Lassad Hassine ◽  
Abdelkader Chaari
Keyword(s):  
Clustering Algorithm ◽  
Kernel Method ◽  
Clustering Algorithms ◽  
Nonlinear System Identification ◽  
Model Identification ◽  
Fuzzy Model ◽  
Nonlinear Behavior ◽  
Optimization Method ◽  
Kernel Clustering ◽  
Takagi Sugeno

In engineering field, it is necessary to know the model of the real nonlinear systems to ensure its control and supervision; in this context, fuzzy modeling and especially the Takagi-Sugeno fuzzy model has drawn the attention of several researchers in recent decades owing to their potential to approximate nonlinear behavior. To identify the parameters of Takagi-Sugeno fuzzy model several clustering algorithms are developed such as the Fuzzy C-Means (FCM) algorithm, Possibilistic C-Means (PCM) algorithm, and Possibilistic Fuzzy C-Means (PFCM) algorithm. This paper presents a new clustering algorithm for Takagi-Sugeno fuzzy model identification. Our proposed algorithm called Robust Kernel Possibilistic Fuzzy C-Means (RKPFCM) algorithm is an extension of the PFCM algorithm based on kernel method, where the Euclidean distance used the robust hyper tangent kernel function. The proposed algorithm can solve the nonlinear separable problems found by FCM, PCM, and PFCM algorithms. Then an optimization method using the Particle Swarm Optimization (PSO) method combined with the RKPFCM algorithm is presented to overcome the convergence to a local minimum of the objective function. Finally, validation results of examples are given to demonstrate the effectiveness, practicality, and robustness of our proposed algorithm in stochastic environment.

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