Type-2 Takagi-Sugeno-Kang Fuzzy Logic Modeling using Subtractive Clustering

Ren, Qun; Baron, Luc; Balazinski, Marek

doi:10.1109/nafips.2006.365871

Cited by 46 publications

(19 citation statements)

References 9 publications

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“…However, Karnik-Mendel method requires iterative calculations that may be time consuming. In this paper, the following calculations [25,27] is selected for simplification:…”

Section: T-s Fuzzy Modeling For An Mimo Processmentioning

confidence: 99%

“…The increased fuzziness endows a Type-2 fuzzy set additional design degrees of freedom that make it possible to directly describe the uncertainties [20][21][22][23]. [24] gave an introduction of Type-2 T-S fuzzy models, and several results [25][26][27] proved that Type-2 T-S fuzzy model outperforms its Type-1 counterpart in terms of accuracy and robustness in process modeling and control. This paper investigates both Type-1 and Type-2 ETSM for decentralized control.…”

Section: Introductionmentioning

confidence: 99%

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Type-1 and Type-2 effective Takagi-Sugeno fuzzy models for decentralized control of multi-input-multi-output processes

Liao

Sun

Cai

et al. 2017

Journal of Process Control

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Effective Takagi-Sugeno (T-S) fuzzy model Type-2 fuzzy system Decentralized control a b s t r a c t Effective model is a novel tool for decentralized controller design to handle the interconnected interactions in a multi-input-multi-output (MIMO) process. In this paper, Type-1 and Type-2 effective Takagi-Sugeno fuzzy models (ETSM) are investigated. By means of the loop pairing criterion, simple calculations are given to build Type-1/Type-2 ETSMs which are used to describe a group of non-interacting equivalent single-input-single-output (SISO) systems to represent an MIMO process, consequently the decentralized controller design can be converted to multiple independent single-loop controller designs, and enjoy the well-developed linear control algorithms. The main contributions of this paper are: i) Compared to the existing T-S fuzzy model based decentralized control methods using extra terms to characterize interactions, ETSM is a simple feasible alternative; ii) Compared to the existing effective model methods using linear transfer functions, ETSM can be carried out without requiring exact mathematical process functions, and lays a basis to develop robust controllers since fuzzy system is powerful to handle uncertainties; iii) Type-1 and Type-2 ETSMs are presented under a unified framework to provide objective comparisons. A nonlinear MIMO process is used to demonstrate the ETSMs' superiority over the effective transfer function (ETF) counterparts as well as the evident advantage of Type-2 ETSMs in terms of robustness. A multi-evaporator refrigeration system is employed to validate the practicability of the proposed methods.

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“…However, Karnik-Mendel method requires iterative calculations that may be time consuming. In this paper, the following calculations [25,27] is selected for simplification:…”

Section: T-s Fuzzy Modeling For An Mimo Processmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Type-1 and Type-2 effective Takagi-Sugeno fuzzy models for decentralized control of multi-input-multi-output processes

Liao

Sun

Cai

et al. 2017

Journal of Process Control

View full text Add to dashboard Cite

show abstract

“…In the antecedent parts, the input space is divided into a set of fuzzy regions, and in the consequent parts the system behavior in those regions is described. Recently a number of different approaches have been used for designing fuzzy IF-THEN rules based on clustering [35][36][37][38][39][40], the table look-up scheme [41], the least-squares method (LSM) [1,16], gradient algorithms [2,3,7,18,19,29], and genetic algorithms [5,29,31]. In this paper, the fuzzy clustering is applied to design the antecedent (premise) parts, and the gradient algorithm is applied to design the consequent parts of the fuzzy rules.…”

Section: Parameter Update Rulesmentioning

confidence: 99%

“…Clustering has been well used for type-1 fuzzy systems. For type-2 fuzzy systems, subtractive clustering and fuzzy clustering have been developed recently [37][38][39]. Subtractive clustering [40] is an extension of the grid based mountain clustering.…”

Section: Parameter Update Rulesmentioning

confidence: 99%

“…Therefore various types of uncertainties may have to be taken into account. To this end, the use of interval type-2 FCM is proposed for pattern recognition [37][38][39]. In this paper, type-2 FCM is used to select the cluster centres of the membership functions in the antecedent part of fuzzy rules of the type-2 FWNN using the input data set of the plant.…”

Section: Parameter Update Rulesmentioning

confidence: 99%

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