• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1997년도 춘계학술대회 학술발표 논문집
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• pp.63-66
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• 1997

The rule bases self organizing controller(SOC) has one of its main advantages in the fact that there is no need to have a mathematical description of the system to be controlled. In this controller, the rules are linguistics statements expressed mathematically through the concepts of fuzzy sets and correspond to the actions a human operator would take when controlling a given process. With this controller, we have performed to sight stabilization system, and we realize that it needs a scale factor tuning. The self tuning controller(STC) uses an instantaneous system fuzzy performance which can give an inspection to the scale factor. Therefore, the STC can compensate the scale factor when it is not adequately tuned. With this trial, we shows that STC can give a good transient characteristics in the nonlinearity which imposed basically in the conventional servo system.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2005년도 전력전자학술대회 논문집
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• pp.693-696
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• 2005

We propose a robust adaptive fuzzy controller for the transient stability and voltage regulation of a single-machine inflnite bus power system. The proposed control scheme is based on the input-output linearization to eliminate the system nonlinearities. To deal with uncertainties due to a parameter variation or a fault, we introduce fuzzy systems with universal function approximating capability which estimate the uncertainties on-line.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2005년도 전력전자학술대회 논문집
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• pp.697-700
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• 2005

An adaptive fuzzy sliding-mode controller (SMC) for uncertain or ill-defined single-input single-output (SISO) nonaffine nonlinear systems is proposed. By using the universal approximation property of the fuzzy logic system (FLS), it is tuned on-line to cancel the unknown system nonlinearity. We adopt a self-structuring FLS to guarantee global stability of the closed-loop system rather than semi=global boundedness. The control and adaptive laws are derived so that the estimated fuzzy parameters are bounded and the sliding condition is satisfied.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 C
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• pp.1118-1120
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• 1998

In this study, an adaptive evolutionary computation (AEC), which uses adaptively a genetic algorithm having global searching capability and an evolution strategy having local searching capability with different methodologies, is suggested. We applied the AEC to design of fuzzy logic controllers for a PSS (power system stabilizer). FLCs for PSS controllers are designed for damping the low frequency oscillations caused by disturbances such as tile sudden changes of loads, outages in generators, transmission line faults, etc. The membership functions of FLCs is optimally determined by AEC.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2819-2821
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• 2000

We introduce Adaptive Fuzzy Impedance Controller for force control when robot contact with environment. Because robot and environment was always effected by nonlinear conditions. it needs to deal with parameter's uncertainty. As. it induced Fuzzy system in impedance controller. it used fuzzy inference logic that has robustness about uncertainty to tune impedance controller stiffness gain. We applied adaptive fuzzy impedance controller in One-Link Robot system and the method shows a good performance on desired position and force control with intensional contacting environment.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2374-2376
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• 2000

In this paper, we propose an indirect vector control method using Adaptive Neuro-Fuzzy Inference System (ANFIS) parameter estimator. It estimates the rotor time constant when the indirect vector control of induction motor is applied. We use the stator current error that is difference between the current command and estimated current calculated from terminal voltage and current. And two induced current estimate equations are used in training ANFIS.The estimator is trained by the hybrid learning algorithm. Simulation results shows good performance under load disturbance and motor parameter variations.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 추계학술대회 논문집 학회본부 B
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• pp.490-492
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• 1998

In this paper, fuzzy-PD control system is designed to control angle and position of the inverted pendulum. To optimize parameters of fuzzy-PD controller, we used adaptive evolutionary computation(AEC). AEC uses a Genetic A1gorithm(GA) and an Evolution Strategy(ES) in an adaptive manner in order to take merits of two different evolutionary computations.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.813-816
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• 1995

The shape and width of fuzzy membership function has an effect on performance of fuzzy controller. In this paper, neuro-fuzzy controller is proposed to improve the control performance of fuzzy controller. It has membership function, that is adapt to plant constant by using trained neural network. This neural network has been trained with back propagation algorithm. To show the effectiveness of proposed neuro-fuzzy controller with adaptive membership function, it is applied to plant (dead time + 1st order) with various plant constant.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.702-704
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• 1995

In this paper, using a new adaptive fuzzy controller we have designed a power system stabilizer. The adaptive fuzzy controller constitutes of several parallel fuzzy controller. Each of them can maintain the robust stability for a specified parametric uncertainty region. If the parametric variation is so large that a rule-base cannot cope with that parametric region, the other appropriate rule-base is selected to control. Applying adaptive fuzzy controller to single machine infinite bus system, we simulate the stability of the system and compare the performance with conventional PSS controller.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2002년도 추계학술대회 및 정기총회
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• pp.84-87
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• 2002

This paper presents an adaptive PID control scheme with fuzzy model for nonlinear system. TSK(Takagi-Sugeno-Kang) fuzzy model was used to estimate the error of control input, and the parameter of PID controller was adapted from the error The parameter of TSK fuzzy model was also adapted to plant by comparing the activity output of plant and model output. PID controller which was adapted the uncertainty of nonlinear plant and the change of parameter can be designed by using the presented method. The usefullness of algorithm which was proposed by the simulation of several nonlinear system was also certificated.