• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.183-187
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• 2000

본 논문에서는 불확실성을 가지는 TS 퍼지 시스템을 안정화시킬 수 있는 제어기를 제안하고, 선형행렬부등식(Linear Matrix Inequality : LMI)을 이용한 설계 방법을 제시하였다. 그리고, 간단한 예제를 통하여 제안된 기법의 응용 가능성을 확인하여 보았다.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.6
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• pp.477-486
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• 2001

In this paper , we study the problem of designing TS(Takagi-Sugeno) fuzzy controllers for the systems that can be approximated or represented by the TS fuzzy model. The main strategy used in this paper is the inverse optimal approach, in which the cost function is determined later than the Lyapunov function and its corresponding control input satisfying the design requirements such as stability, decay rate, and robustness against uncertainty. This approach is useful because it yields controllers satisfying the inherent robustness of optimal controllers as well as the considered design goals. The design procedures established in this paper are all in the from of solving LMIs(Iinear matrix inequalities). Since the LMIs arising in the design procedures can be solved within a given tolerance by the interior point methods. the design method of the paper are efficient in practice. The applicability of the proposed design procedures is demonstrated by design examples.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.469-474
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• 2011

This paper presents the intelligent tracking method for maneuvering target using the positional error compensation of the maneuvering target. The difference between measured point and predict point is separated into acceleration and noise. K-means clustering and TS fuzzy system are used to get the optimal acceleration value. The membership function is determined for acceleration and noise which are divided by K-means clustering and the characteristics of the maneuvering target is figured out. Divided acceleration and noise are used in the tracking algorithm to compensate computational error. While calculating expected value, the non-linearity of the maneuvering target is recognized as linear one by dividing acceleration and the capability of Kalman filter is kept in the filtering process. The error for the non-linearity is compensated by approximated acceleration. The proposed system improves the adaptiveness and the robustness by adjusting the parameters in the membership function of fuzzy system. Procedures of the proposed algorithm can be implemented as an on-line system. Finally, some examples are provided to show the effectiveness of the proposed algorithm.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.137-140
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• 2001

In this paper, we design 75(Takagi-Sugeno) fuzzy controllers for the systems that can be represented by the 75 fuzzy model. We use inverse optimal approach in which the cost function is determined later than the Lyapunov function and its corresponding control input satisfying the design requirements such as stability and decay rate. The obtained design procedure is in the form of solving LMI(Linear Matrix Inequalities), thus very efficient in practice.

• Journal of Industrial Technology
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• v.28 no.B
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• pp.101-109
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• 2008

In general, non-stationary or chaos time series forecasting is very difficult since there exists a drift and/or nonlinearities in them. To overcome this situation, we suggest a new prediction method based on multiple model TS fuzzy predictors combined with preprocessing of time series data, where, instead of time series data, the differences of them are applied to predictors as input. In preprocessing procedure, the candidates of optimal difference interval are determined by using con-elation analysis and corresponding difference data are generated. And then, for each of them, TS fuzzy predictor is constructed by using k-means clustering algorithm and least squares method. Finally, the best predictor which minimizes the performance index is selected and it works on hereafter for prediction. Computer simulation is performed to show the effectiveness and usefulness of our method.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.3
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• pp.318-323
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• 2005

In this paper, we propose a systematic method of a fuzzy-model-based controller for continuous-time nonlinear dynamical systems which may contain uncertainties. The continuous-time uncertain TS fuzzy model is first constructed to represent the uncertain nonlinear system. A parallel distributed compensation (PDC) technique is then used to design a fuzzy model based controller for both stabilization and tracking. Finally, the designed continuous-time controller is converted to an equivalent discrete-time controller by using an intelligent digital redesign method. This new design technique provides a systematic and effective framework for integration of the fuzzy model based control theory and the advanced digital redesign technique for nonlinear dynamical systems with uncertainties. Finally, the single link flexible-joint robot arm is used as an illustrative example to show the effectiveness and the feasibility of the developed design method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.11a
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• pp.411-414
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• 2007

본 논문은 뉴트럴 타입 시간 지연을 갖는 네트워크 시스템의 안정도 분석 및 퍼지 제어기 설계에 대해서 논의한다. 먼저 대상이 되는 네트워크 시스템은 TS (Takagi-Sugeno: T-S) 퍼지 모델로 표현 되어진다. 리아프노프-크라조브스키의 안정도 이론을 이용하여 뉴트럴 형태의 시간 지연을 갖는 퍼지 시스템의 안정도를 판별한다. 퍼지 시스템의 안정도 조건을 시간 지연에 종속적인 충분조건으로 제시하고 선형 행렬 부등식의 형태로 표현한다. 선형 행렬 부동식의 해를 구하고 이를 바탕으로 퍼지 제어기의 이득값을 설계한다. 제안된 방법의 효율성과 가능성을 보여주기 위해 한 예제를 포함한다.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.3
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• pp.267-272
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• 2012

This paper propose the implementation of robust fuzzy controller for controlling an active magnetic bearing (AMB) system with 6 degree of freedom (DOF). A basic model with 6 DOF rotor dynamics and electromagnetic force equations for conical magnetic bearings is proposed. The developed model has severe nonlinearity and uncertainty so that it is not easy to obtain the control objective. For solving this problem, we use the Takagi-Sugeno (T-S) fuzzy model which is suitable for designing fuzzy controller. The control object in the AMB system enables the rotor to rotate without any phsical contact by using magnetic force. In this paper, we analyze the nonlinearity of the active magnetic bearing system by using fuzzy control algorithm and desing the robust control algorithm for solving the parameter variation. Simulation results for AMB are demonstrated to visualize the feasibility of the proposed method.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2215-2217
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• 2004

In this paper, a systematic design approach based on the parallel distributed compensation technique is proposed for chaotifying a general continuous-time Takagi-Sugeno (TS) fuzzy system. The fuzzy parallel distributed compensation controller (FPDCC) is composed of the feedback gain and time-delay feedback. The verification of chaos in the controlled continuous-time TS fuzzy system is done by the following procedures. First, we establish an asymptotically approximate relationship between a time-delay continuous-time TS fuzzy system and a discrete-time TS fuzzy system. Then, Marotto theorem is applied. Therefore, the generated chaos is in the sense of Li and Yorke. The boundedness in the controlled continuous-time TS fuzzy system is also proven via its associated discrete-time TS fuzzy system.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.881-886
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• 2005

This paper presents intelligent digital redesign method of global approach for hybrid state space fuzzy-model-based controllers. For effectiveness and stabilization of continuous-time uncertain nonlinear systems under discrete-time controller, Takagi-Sugeno(TS) fuzzy model is used to represent tile complex system. And global approach design problems viewed as a convex optimization problem that we minimize the error of the norm bounds between nonlinearly interpolated linear operators to be matched. Also, by using the power series, we analyzed nonlinear system's uncertain parts more precisely. When a sampling period is sufficiently small, the conversion of a continuous-time structured uncertain nonlinear system to an equivalent discrete-time system have proper reason. Sufficiently conditions for the global state-matching of tile digitally controlled system are formulated in terms of linear matrix inequalities (LMIs). Finally, a TS fuzzy model for the chaotic Lorentz system is used as an example to guarantee the stability and effectiveness of the proposed method.