• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.2
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• pp.185-190
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• 2009

In this paper, an observer-based intelligent controller for the nonlinear networked control systems with packet loss is proposed for wireless sensor network. For the intelligent control of the nonlinear system, it uses the fuzzy system with Takagi-Sugeno (T-S) fuzzy model. The observer is designed for the fuzzy networked control system, and the output feedback controller is proposed for the stability of estimates and errors. The stability condition of the closed-loop system with the proposed controller is represented to the linear matrix inequality (LMI) form, and the observer and control gain are obtained by LMI. An example is given to show the verification discussed throughout the paper.

• Journal of Digital Contents Society
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• v.2 no.1
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• pp.51-61
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• 2001

The FLC(Fuzzy Logic Controller) is stronger to the disturbance and has the excellent characteristic to the overshoot of the initialized value than the classical controller, and also can carry out the proper control being out of all relation to the mathematical model and parameter value of the system. But it has the restriction which can't adopt the environment changes of the control system because of generating the fuzzy control rule through an expert's experience and the fixed value of the once determined control rule, and also can't converge correctly to the desired value because of haying the minute error of the controller output value. Now there are many suggested methods to eliminate the minute error, we also suggest the GA-FNNIC(Genetic Algorithm Fuzzy Neural Network Intelligence Controller) combined FLC with NN(Neural Network) and GA(Genetic Algorithm). In this paper, we compare the suggested GA-FNNIC with FLC and analyze the output characteristics, convergence speed, overshoot and rising time. Finally we show that the GA-FNNIC converge correctly to the desirable value without any error.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1758-1759
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• 2011

본 논문은 비선형 시스템의 소속함수 의존성을 이용한 관측기 기반 퍼지 제어기 설계 기법을 제시한다. Takage-Sugeno (T-S)퍼지 모델링을 이용해 비선형 시스템을 퍼지 모델로 표현한다. 시스템의 안정화 조건은 소속함수의 의존성을 이용한 리아푸노프 안정도 해석 방법을 이용해 유도된다. 안정화 조건은 선형 행렬 부등식으로 표현되며, 부등식의 해를 이용해 제어기의 이득값을 구한다. 모의실험을 통해 설계된 제어기의 타당성을 검증한다.

• Journal of the Korean Society of Industry Convergence
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• v.10 no.4
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• pp.271-274
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• 2007

In this paper, we propose a intelligent controller design method for the water level control of the power plant drum in the form of nonminimum phase system. The proposed method is based on T. Takagi and M. Sugeno's fuzzy model. And we illustrate the improved characteristics as the simulation results, comparing with the conventional the PID and LQ controller design method.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2006.05a
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• pp.159-163
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• 2006

본 논문에서는 비선형 시스템의 슬라이딩 모드 관측기 설계에 대해서 논의한다. 제어 대상인 비선형 시스템을 모델링 하는데 있어서 Takagi-Sugeno(T-S) 퍼지 모델 기법을 이용하였고, 이 때 발생할 수 있는 모델 불확실성과 외란에 대해 그것의 최대 최소 범위를 안다고 가정하였다. 제안된 시스템의 LMI (Linear Matrix Inequality)를 기반으로 한 슬라이딩 모드 관측기 설계 방법에서는 관측기와 시스템의 차이를 슬라이딩 표면으로 설정한다. 안정한 슬라이딩 표면을 갖는 슬라이딩 관측기의 존재 가능성을 선형 행렬 부등식의 형태로 표현한다. 슬라이딩 모드 관측기 이득은 LMI 존재 조건의 해를 이용하여 구한다.

• Journal of the Korean Society of Industry Convergence
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• v.9 no.3
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• pp.179-181
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• 2006

In this paper, we present a method of fuzzy controller design for the power plant superheater in the form of bilinear system. For the steam temperature control, the input variables are constructed by the area of difference between the profiles estimated from bilinear observer and reference profiles, and the time rate of change. We estimate the control rules by T. Takagi and M. Sugeno's fuzzy model. The feasibilities of the suggested method are illustrated via the computer simulation result.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2290-2292
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• 2000

Robustness characteristics to the modelling imprecision and some disturbances could be achieved in sliding mode control. However, there are drawbacks such as discontinuous control and chattering. Recently, many researches have been developing to solve such the problems. In sliding mode control, overall control input could be divided into two parts which are equivalent control input and sliding mode control input. Sliding mode control input is a function of the switching surfaces and can be designed with their linear combinations. In this paper, the sliding mode control input is designed by TSK fuzzy model. The proposed method gives the continuous sliding control input and reject the chattering phenomenon.

• Journal of Korean Port Research
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• v.12 no.1
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• pp.1-8
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• 1998

If a system has a large boundary and complexity, forecast's accuracy will be very low when consider the system's substance as black box. Thus, it is necessary that analysis by structure model. To examine competition in Northeast Asia Ports, it has assumed that the form of structure model, For which the System Dynamics method is adapted in this paper. Northeast Asia Ports Model includes five ports - Pusan, Kobe, Yokohama, Kaoshiung, Keelung, - which are adjacent to each other by geographically and has a competition relation. The Northeast Asia Ports Model has several sub-systems which consists of each unit port models. And, each unit port model found by quantitive, qualititive factors and their feedback loops. All effects which components of one port have influence to components of the rest ports must be surveyed in order to construct Northeast Asia Ports Model, but it may be impossible currently. In this paper Northeast Asia Ports Model was simplified by HFP-Hierarchical Fuzzy Process Method-adapted to integration of level variables of unit port model. Container cargo volumes in Northeast Ports Model is distributed by results of HFP method. And distributed container cargo volumes effected to unit port model. Developed model can estimate change of container cargo volumes in competitive relation by alternation of simple parameter, and reflects dynamics characteristics which are included in model.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.5
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• pp.744-752
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• 2012

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as an one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problems. In data preprocessing part, Principal Component Analysis(PCA) which is generally used in face recognition, which is useful to express some classes using reduction, since it is effective to maintain the rate of recognition and to reduce the amount of data at the same time. However, because of there of the whole face image, it can not guarantee the detection rate about the change of viewpoint and whole image. Thus, to compensate for the defects, Linear Discriminant Analysis(LDA) is used to enhance the separation of different classes. In this paper, we combine the PCA&LDA algorithm and design the optimized pRBFNNs for recognition module. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as two kinds of polynomials such as constant, and linear. The coefficients of connection weight identified with back-propagation using gradient descent method. The output of the pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to face image(ex Yale, AT&T) datasets and then demonstrated from the viewpoint of the output performance and recognition rate.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.6
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• pp.753-758
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• 2006

We present the development of the digital fuzzy controller for maximum power regulation. A hybrid system that comprises wind and photovoltaic generation subsystems, and battery bank is developed in this paper. We use Takaki-Sugeno (T-S) fuzzy model to deal with the power regulation problem, since each power generator has complex nonlinear terms. The problem for regulation control can be simplified into a stabilization one. Also, in order to utilize the advanced digital device, we perform the intelligent digital redesign method. Finally, the performance of the proposed controller is extensively assessed through computer simulation.