• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1862-1863
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• 2011

본 논문은 T-S Fuzzy Identification을 이용하여 PMSM를 모델링하고 T-S Fuzzy 제어로 PMSM을 제어하는 것 제안합니다. 시스템을 모델링을 위해서는 기존에는 파라미터를 알아야 가능했지만 시스템의 입출력 데이터를 가지고 T-S Fuzzy Identification을 하게 되면 쉽게 시스템을 모델링 할 수 있다. 논문에서는 T-S Fuzzy Identification을 통하여 모델링을 하고 T-S Fuzzy제어을 통해서 PMSM을 제어 할 수 있는 것을 보여주고 한다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.5
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• pp.973-981
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• 2012

In this paper, it suggest that nonlinear multivariable system control of induction motor using 'T-S Fuzzy Identification' 'T-S Fuzzy model of linearization' is not easy because of that arithmetic is difficult in computation of the function. Therefore 'T-S Fuzzy Identification' is suggested that the rules and functions through the estimation of high accuracy provides linearized model.

• Journal of the Korean Society for Precision Engineering
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• v.28 no.4
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• pp.391-397
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• 2011

Control of interior permanent magnet (IPMSM) is difficult because its nonlinearity and parameter uncertainty. In this paper, a fuzzy c-regression models clustering algorithm which is based on T-S fuzzy is used to model IPMSM with a series linear model and weight them by memberships. Lagrangian of constrained function is built for calculating clustering centers where training output data are considered. Based on these clustering centers, least square method is applied for T-S fuzzy linear model parameters. As a result, IPMSM can be modeled as T-S fuzzy model for T-S fuzzy control of them.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1856-1857
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• 2011

Induction motor is nonlinear multivariable system. It is not easy to control precisely. Usually Induction motor need linearized model in order to make it easy to control. In this paper, linearized model of nonlinear model in induction motor can change by using TS Fuzzy Identification.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.31 no.6
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• pp.61-71
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• 2003

In the study of control technique for a turbojet engine model, the Takagi-Sugeno fuzzy logic controller has been designed based on the model identification by the well designed PI controlled system through T-S neuro-fuzzy inference system. To enhance this designed controller, those procedures are proposed that certainty factors are adopted to each rule of objective groups which are classified by the fuzzy C-Means algorithm and the satisfaction degrees are matched to meet the objectives. This proposed technique shows its feasibility by upgrading performances of the previously well-designed T-S fuzzy controller.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1858-1859
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• 2011

제어를 할 때 비선형 시스템을 선형화 하는 것이 중요하다. 선형화를 하기위해는 퍼지 모델을 사용하는데 그 중 바퀴형 역진자 시스템은 비선형 시스템의 파라미터 값을 모두 알아도 T-S퍼지를 기반으로 하여 선형제어를 사용하는데 어려움이 있다. 그래서 Identification을 함으로써 바퀴형 역진자 시스템을 좀 더 편리하게 T-S 퍼지 모델로 만들 수 있다.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.588-591
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• 2003

This paper presents a new short-term load forecasting system using data mining. Since the electric load has very different pattern according to the day, it definitely gives rise to the forecasting error if only one forecasting model is used. Thus, to resolve this problem, the fuzzy model-based classifier and predictor are proposed for the forecasting of the hourly electric load. The proposed classifier is the multi-input and multi-output fuzzy system of which the consequent part is composed of the Bayesian classifier. The proposed classifier attempts to categorize the input electric load into Monday, Tuesday$\sim$Friday, Saturday, and Sunday electric load, Then, we construct the Takagi-Sugeno (T-S) fuzzy model-based predictor for each class. The parameter identification problem is converted into the generalized eigenvalue problem (GEVP) by formulating the linear matrix inequalities (LMIs). Finally, to show the feasibility of the proposed method, this paper provides the short-term load forecasting example.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.44 no.12
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• pp.1043-1053
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• 2016

In this paper, the application of Parallel Distributed Compensation (PDC) controller for fixed pitch rotary wing system was studied. For nonlinear modeling, T-S fuzzy model was utilized to advance system control including the tilt type UAV. PDC controller was designed through the Linear Matrix Inequality (LMI). Experiments for determining the applicability and feasibility of PDC were performed using the 1 axis attitude control equipment and simulation. To verify the performance and characteristics of the controller, Mathworks Co. Simulink was used. After then, the PDC controller performance was verified and the results with developed controller using a 1 axis attitude control equipment were compared. Verification of the feasibility of PDC controller for the fixed pitch rotary wing system and identification of the overall performance and improvement analysis was conducted based on the experimental results.