• Journal of Advanced Marine Engineering and Technology
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• 제19권2호
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• pp.67-80
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• 1995

In order to control systems which are dominantly subjected to modeling errors and uncertainties, control strategies must deal with the effect of modeling errors and uncertainties. Since most of control methods based on system mathematical model, such as LQG/LTR method, have been developed mainly focused on stability robustness, they can not smartly improve the transient response disturbed by modeling errors and/or uncertainties. In this research, a fuzzy PID control method is suggested, which can stably improve the transient responses of systems disturbed by modeling errors as well as systems not entirely using mathematical models. So as to assure the effectiveness of suggested control method, computer simulations are accomplished for some example systems, through the comparison of transient responses.

• 한국지능시스템학회논문지
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• 제10권5호
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• pp.432-441
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• 2000

본 논문은 기존의 수학적인 모델링으로는 만족스러운 결과를 얻기 어려운 복잡하고 불확실한 비선형 시스템에 대한 퍼지 모델링 기법을 다룬다. 유전 알고리듬은 어느 정도 최적해를 전역적으로 찾을 수 있기 때문에 퍼지 모델링시에 파라미커와 구조를 동정하기 위하여 사용되었다. 하지만, 유전 알고리듬은 개체군이 유전적 다양성을 잃었을 경우 조기 수렴한다는 문제점이 있으며 바이러스-진화 유전 알고리듬은 이러한 지역수렴에 대한 방아닝 될 수 있다. 따라서, 본 논문에서는 바이러스 이론이 적용된 VEGA를 퍼지 모델링 할 때 이용할 수 있는 방법을 제안한다. 이 방법에서는 지역정보가 개체군 내에서 교환됨으로써 유전적 다양성을 유지하게 된다. 마지막으로, 본 논문에서 제안한 방법의 우수성과 일반성을 평가하기 위해 몇 가지의 수치적 예제를 제공한다.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2004년도 ICEIC The International Conference on Electronics Informations and Communications
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• pp.348-353
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• 2004

This paper proposes an alternative observation scheme, T-S fuzzy model based indirect adaptive fuzzy observer. Nonlinear systems arc represented by fuzzy models since fuzzy logic systems arc universal approximators. In order to estimate the unmeasurable states of a given nonlinear system, T-S fuzzy modeling method is applied to get the dynamics of an observation system. T-S fuzzy system uses the linear combination of the input state variables and the modeling applications of them to various kinds of nonlinear systems can be found. The adaptive fuzzy scheme estimates the parameters comprising the fuzzy model representing the observation system. The proposed indirect adaptive fuzzy observer based on T-S fuzzy model can cope with not only unknown states but also unknown parameters. In the process of deriving adaptive law, the Lyapunov theory and Lipchitz condition are used. To show the performance of the proposed observation method, it is applied to an inverted pendulum on a cart.

• 한국세라믹학회지
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• 제41권12호
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• pp.873-879
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• 2004

In all major industries ranging from powder industries and advanced ceramics, to the food and pharmaceutical manufacture powder industries, the main production process is the spray dryers. In this paper, a systematic approach is used and six rules are obtained for the basis of the fuzzy model. A fuzzy model is based on the past behavior of the target system and expected to be able to reproduce the behavior of the target system. The output of the developed fuzzy model shows, graphically and statistically, a high level of face validity. Therefore, it is concluded that the developed fuzzy model mimics the actual process and can be considered, with confidence, as a reliable model to study, analyze, and improve the existing process.

• 전자공학회논문지C
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• 제34C권5호
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• pp.9-18
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• 1997

The petri net is a graphic model which is adaptable in modeling a complex concurrent parallel ssystem, and it is widely used in the fields of industrial enginering, computer science, electric engineeringand chemistry. Recently, the net is applied to the communication protocol, and extended to represent complex systems. There are several extended petri nets named as TPN (timed petri net), SPN (stochastic petri net), FPN(fuzzy petri net) and TFPN(timed fuzzy petri net). Accodingly, this SPN (stochastic petri net), FPN (fuzzy petri net) and TFPN(timed fuzzy petri net). Accodingly, this paper proposes an advanced communication protocol modeling method using the fuzzy value of the transition and firing delay time as the arguments of the function. The proposed method can produce clearer firing rules, and it is supposed to be used to design and analyse communication protocols in great effection.

• Journal of Mechanical Science and Technology
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• 제15권8호
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• pp.1132-1142
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• 2001

The paper describes the construction of global function approximation models for use in design optimization via global search techniques such as genetic algorithms. Two different approximation methods referred to as evolutionary fuzzy modeling (EFM) and neuro-fuzzy modeling (NFM) are implemented in the context of global approximate optimization. EFM and NFM are based on soft computing paradigms utilizing fuzzy systems, neural networks and evolutionary computing techniques. Such approximation methods may have their promising characteristics in a case where the training data is not sufficiently provided or uncertain information may be included in design process. Fuzzy inference system is the central system for of identifying the input/output relationship in both methods. The paper introduces the general procedures including fuzzy rule generation, membership function selection and inference process for EFM and NFM, and presents their generalization capabilities in terms of a number of fuzzy rules and training data with application to a three-bar truss optimization.

• Journal of Electrical Engineering and Technology
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• 제5권2호
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• pp.276-281
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• 2010

The switched reluctance motor (SRM) is an older member of the electric machines family. Its simple structure, ruggedness and inexpensive manufacturing potential make it extremely attractive for industrial applications. However, these merits are overshadowed by its inherent high torque ripple, acoustic noise and difficulty to control. In this paper, a control strategy of the angle position control for the SRM drive based on fuzzy logic is illustrated. The input control parameter, the output control parameter and fuzzy control with FAM table formulation strategy are described and simulated with control patterns, and the decision form of the fuzzy control is illustrated and simulated, and the scope of implementing in a Fuzzy based ASIC chip is enlightened with literature support.

• 한국정밀공학회지
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• 제14권4호
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• pp.110-118
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• 1997

Multi-layer neural networks with error back-propagation algorithm have a great potential for identifying nonlinear systems with unknown characteristics. However, because they have a demerit that the speed of convergence is too slow, various methods for improving the training characteristics of backpropagition networks have been proposed. In this paper, a fuzzy division method is proposed to improve the convergence speed, which can find out an effective fuzzy division by the tuning of membership function and independently train each neural network after dividing the network model into several parts. In the simulations, the proposed method showed that the optimal fuzzy partitions could be found from the arbitray initial ones and that the convergence speed was faster than the traditional method without the fuzzy division.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2001년도 추계학술대회논문집A
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• pp.716-722
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• 2001

In this paper, the modeling and control of electrostrictive polymer is introduced for endoscopic microcapsule. The endoscopic microcapsule works in the body, so the material of robot must be no harmful to the body. The electrostrictive polymer satisfies this condition. The modeling and control of endoscope microcapsule must be processed. So the modeling and control of electrostrictive was processed preferentially. The electrostrictive polymer is so flexible that we considered the electrostrictive polymer as flexible membrane. The dynamic equation of flexible membrane is time variant in electrostrictive polymer. It is the reason that the elastic modulus of electrostrictive polymer is very small and changes as deformation of electrostrictive polymer. The control algorithm must overcome these characteristics. So the algorithm of adaptive fuzzy control was used to control. In this paper, we introduced the dynamic modeling and control of electrostrictive polymer. And its deformation is introduced.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2004년도 추계학술대회 학술발표 논문집 제14권 제2호
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• pp.192-196
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• 2004

This paper proposes an alternative observation scheme, T-S fuzzy model based indirect adaptive fuzzy observer. Nonlinear systems are represented by fuzzy models since fuzzy logic systems are universal approximators. In order to estimate the unmeasurable states of a given nonlinear system, T-S fuzzy modeling method is applied to get the dynamics of an observation system. T-S fuzzy system uses the linear combination of the input state variables and the modeling applications of them to various kinds of nonlinear systems can be found. The adaptive fuzzy scheme estimates the parameters comprising the fuzzy model representing the observation system. The proposed indirect adaptive fuzzy observer based on T-S fuzzy model can cope with not only unknown states but also unknown parameters. In the process of deriving adaptive law, the Lyapunov theory and Lipchitz condition are used. To show the performance of the proposed observation method, it is applied to an inverted pendulum on a cart.