Type2 TSK Fuzzy Logic System and its Type1 Counterpart

Ren, Qun; Balazinski, Marek; Baron, Luc

doi:10.5120/2440-3292

Cited by 13 publications

(14 citation statements)

References 18 publications

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“…In the literature, several approaches have been proposed that use fuzzy logic techniques for modeling, identification, and control of systems [4][5][6][7][8][9][10][11][12][13][14]. In [4], simple parameterized conjunctions were used for nonlinear function approximation.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Interval TSK Type‐2 Modeling with Parameterized Conjunctors

Aras¹,

Kaynak²

2014

Asian Journal of Control

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In this study, a novel approach is described to the design of an interval type-2 fuzzy neural system (IT2 FNS). It differs from the classical IT2 FNS in its use of parameterized conjunctors. In the optimization of the IT2 FNS, the membership functions are kept fixed and only the parameters of the conjunctors and the parameters in the consequent are tuned. In this study, the gradient based learning algorithm is used. The approach is tested for the modeling of a benchmark nonlinear function and for the wheel slip control of a quarter car model (QCM). In the stated applications, in the absence of any expert knowledge, some knowledge about the system is gained by the use of the interval type-2 fuzzy c-means (IT2 FCM) clustering algorithm. Nevertheless, this requires the number of classes to be known beforehand. To alleviate this problem, some validity indices that have been suggested in the literature and a novel validity index that carries less computational burden are considered to determine the number of classes and the number of fuzzy rules. Simulation studies are presented and compared with the results from the literature.

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Section: Introductionmentioning

confidence: 99%

Fuzzy Interval TSK Type‐2 Modeling with Parameterized Conjunctors

Aras¹,

Kaynak²

2014

Asian Journal of Control

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“…4, for the group of input-output training data, there are two inputs 12 , UU and one output f U . In a type-2 TSK fuzzy logic, IF-THEN rules are also used to describe the relationship between input and output data.…”

Section: A Interval Type-2 Tsk Fuzzymentioning

confidence: 99%

“…Then, within the time interval 12 [ , ] tt , we morph the winglets of aircraft, so that the winglets completely switch to stretching mode after time 2 t . Assumption: s , c , and all the aerodynamic parameters are switched according to the following format: U as the control law of retracting mode, and 2 U as the control law of stretching mode.…”

Section: Sliding Mode Control Law Of Hypersonic Aircraftmentioning

confidence: 99%

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Learning Control Law of Mode Switching for Hypersonic Morphing Aircraft Based on Type-2 TSK Fuzzy Neural Network

Jiao¹,

Jiang²

2015

IJMLC

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Abstract-A novel learning method of control law of mode switching for hypersonic morphing aircraft, based on type-2 Takagi-Sugeno -Kang (TSK) fuzzy neural network, is proposed in this paper. The purpose of this method is to learn the control law of mode switching from a group of training data, in order to steadily and smoothly switch the winglets from retracting to stretching mode. In this method, taking into consideration the characteristics of type-2 fuzzy, we utilize an interval type-2 TSK fuzzy approach, the rules of which are learned from training data by back-propagation algorithm. Simulation results indicate that the proposed learning method of switching control law, based on type-2 TSK fuzzy neural network, can steadily and smoothly switch the winglets from retracting to stretching mode, providing a novel method for obtaining an excellent switching control law in situations with a group of training data.Index Terms-Control law of mode switching, learning rules, interval type-2 TSK fuzzy, back-propagation algorithm, hypersonic morphing aircraft.

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“…Interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy, 21,22 as a novel kind of fuzzy, has been proved to be more suitable to deal with uncertainty problems 23,24 and has been widely used in control processes. Traditional type-1 TSK fuzzy logic system 25 has been used to resolve the control issue.…”

mentioning

confidence: 99%

Design of adaptive switching control for hypersonic aircraft

Jiao

Jiang

2015

Advances in Mechanical Engineering

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This article proposes a novel adaptive switching control of hypersonic aircraft based on type-2 Takagi-Sugeno-Kang fuzzy sliding mode control and focuses on the problem of stability and smoothness in the switching process. This method uses full-state feedback to linearize the nonlinear model of hypersonic aircraft. Combining the interval type-2 TakagiSugeno-Kang fuzzy approach with sliding mode control keeps the adaptive switching process stable and smooth. For rapid stabilization of the system, the adaptive laws use a direct constructive Lyapunov analysis together with an established type-2 Takagi-Sugeno-Kang fuzzy logic system. Simulation results indicate that the proposed control scheme can maintain the stability and smoothness of switching process for the hypersonic aircraft.

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Type2 TSK Fuzzy Logic System and its Type1 Counterpart

Cited by 13 publications

References 18 publications

Fuzzy Interval TSK Type‐2 Modeling with Parameterized Conjunctors

Fuzzy Interval TSK Type‐2 Modeling with Parameterized Conjunctors

Learning Control Law of Mode Switching for Hypersonic Morphing Aircraft Based on Type-2 TSK Fuzzy Neural Network

Design of adaptive switching control for hypersonic aircraft

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