• 대한전기학회논문지:전력기술부문A
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• 제49권5호
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• pp.205-211
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• 2000

This paper presents an optimal design method for fuzzy logic controller (FLC) of HVDC using an Adaptive Evolutionary Algorithm(AEA). We have proposed the AEA which uses a genetic algorithm (GA) and an evolution strategy (ES) in an adaptive manner in order to take merits of two different evolutionary algorithms. The AEA is used for tuning fuzzy membership functions and scaling constants. Simulation results show that disturbances are well damped and the dynamic performances of FLC have better responses than those of PD controller when AC system load changes suddenly.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1991년도 하계학술대회 논문집
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• pp.708-712
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• 1991

This paper presents a new adaptive structure with fuzzy logic applied to adaptive controller. In the conventional adaptive control, good performance cannot be expected due to the adaptation gain of gradient algorithm fined as a constant. To change adaptation gain property, fuzzy rules, which are based on the output error and its rate of change, have been established. The proposed fuzzy adaptive law shows fast parameter convergence and improved performance. The fuzzy logic base is added to the conventional adaptive structure and little additional computation time is required.

• 전자공학회논문지SC
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• 제42권5호
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• pp.13-18
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• 2005

최근 수 년 동안 이족보행 로봇 제어는 로봇 분야에서 각광을 받는 분야인 한편, 어려운 분야이기도 하다. 본 논문에서는 이족보행 로봇을 위한 적응 퍼지 논리를 이용한 새로운 강인한 제어 방법을 제안한다. 적응 퍼지 논리는 알려지지 않은 불확실성을 제거하기 위한 시스템 추정기로 사용된다. 우선 발바꿈과 불확실성, 외란 등의 영향을 포함한 로봇 모델을 제안한다. 다음, 관절의 속도 측정을 하지 않는 제어기를 설계한다. 퍼지 논리를 튜닝하기 위하여 퍼지 추정 오차 관측기를 시스템에 포함시켰다. 마지막으로 제어방법의 타당성을 보이기 위하여 시뮬레이션 결과를 보여준다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 하계학술대회 논문집 D
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• pp.2064-2066
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• 2002

This paper proposes Model Reference Adaptive Fuzzy System(MRAFS) using Fuzzy Logic Controller(FLC) as a adaptive laws in Model Reference Adaptive System(MRAS) in order to realize the speed-sensorless control of an induction motor. MRAFS estimates the speed of an induction motor with a rotor flux of a reference model and adjustable model in MRAS. Fuzzy logic controller reduces the error of the rotor flux between the reference model and the adjustable model using the error and the change of error as the input of FLC. The computer simulation is executed to verify the propriety and the effectiveness of the proposed system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 B
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• pp.1051-1052
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• 2006

This paper deals with mathmatical of an induction motor, considering non-linearity in the torque balance equation under closed loop operation with a reference speed. A controller based on Adaptive Nuro-Fuzzy Inference System (ANFIS) is developed to minimize overshoot and settling time following sudden changes in load torque. The overall system is modeled and simulated using the Matlab/simulink and Fuzzy Logic Toolbox. The advantages of fuzzy logic and neural network based fuzzy logic controller. Required training data the ANFIS controller is generated by simulation of the anti-windup PI controller is eliminated using the ANFIS controller. The transient deviation of the response from the set reference following variation in load torque is found to be negligibly samll along with a desirable reduction in settling time for the ANFIS controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2001년도 하계학술대회 논문집 D
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• pp.1929-1931
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• 2001

An adaptive fuzzy logic controller to regulate molten steel level in the strip casting process is presented, where parameters of fuzzy controllers are adapted stably by using Lyapunov-stability theory and a switching controller is used together to deal with the approximation error of fuzzy logic system. The level error is proven to converge to zero asymptotically. In the simulation, the clogging/unclogging of a stopper nozzle is considered and overcome by the proposed controller. Robustness to uncertainty is shown to be superior to conventional PI controller.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 ITC-CSCC -2
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• pp.677-680
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• 2000

The technique of reinforcement learning algorithm is extended to solve the multiobjective control problem for uncertain dynamic systems. A multiobjective adaptive critic structure is proposed in order to realize a max-min method in the reinforcement learning process. Also, the proposed reinforcement learning technique is applied to a multiobjective satisfactory fuzzy logic controller design in which fuzzy logic subcontrollers are assumed to be derived from human experts. Some simulation results are given in order to show effectiveness of the proposed method.

• 조명전기설비학회논문지
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• 제23권11호
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• pp.9-21
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• 2009

This paper proposes the alternative observer and controller design scheme based on T-S fuzzy model. Nonlinear systems are represented by fuzzy models since fuzzy logic systems are universal approximators. In order to estimate the unmeasurable states of a given unknown 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 proposed indirect adaptive fuzzy observer based on T-S fuzzy model can cope with not only unknown states but also unknown parameters. The proposed controller is based on a simple output feedback method. Therefore, it solves the singularity problem, without any additional algorithm, which occurs in the inverse dynamics based on the feedback linearization method. The adaptive fuzzy scheme estimates the parameters and the feedback gain comprising the fuzzy model representing the observation system. In the process of deriving adaptive law, the Lyapunov theory and Lipchitz condition are used. To show the performance of the proposed observer and controller, they are applied to an inverted pendulum on a cart.

• Journal of information and communication convergence engineering
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• 제3권1호
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• pp.43-48
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• 2005

In this paper, the adaptive fuzzy logic controller(AFLC) is proposed, which uses real-coding genetic algorithm showing a good performance on convergence velocity and diversity of population among evolutionary computations. The effectiveness of the proposed AFLC was demonstrated by computer simulation for speed control system of AC servo motor. As a result of simulation for the AC servo motor, it is shown the proposed AFLC has the better performance on overshoot, settling time and rising time than the PI controller which is used when tuning AFLC.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제9권4호
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• pp.309-314
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• 2009

This paper presents implementation of the adaptive neuro-fuzzy control method. Control performance of the adaptive neuro-fuzzy control method for a popular inverted pendulum system is evaluated. The inverted pendulum system is designed and built as an education kit for educational purpose for engineering students. The educational kit is specially used for intelligent control education. Control purpose is to satisfy balancing angle and desired trajectory tracking performance. The adaptive neuro-fuzzy controller has the Takagi-Sugeno(T-S) fuzzy structure. Back-propagation algorithm is used for updating weights in the fuzzy control. Control performances of the inverted pendulum system by PID control method and the adaptive neuro-fuzzy control method are compared. Control hardware of a DSP 2812 board is used to achieve the real-time control performance. Experimental studies are conducted to show successful control performances of the inverted pendulum system by the adaptive neuro-fuzzy control method.