• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.441-446
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• 1992

We propose a control architecture for the fuzzy logic control of robot manipulators and a rule base derivation method for a fuzzy logic controller(FLC) using a neural network. The control architecture is composed of FLC and PD(positional Derivative) controller. And a neural network is designed in consideration of the FLC's structure. After the training is finished by BP(Back Propagation) and FEL(Feedback Error Learning) method, the rule base is derived from the neural network and is reduced through two stages - smoothing, logical reduction. Also, we show the performance of the control architecture through the simulation to verify the effectiveness of our proposed method.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.603-610
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• 2001

Industrial processes are normally operated by skilled humans who have the cumulative and logical information about the system. Fuzzy control has been investigated for many application. Intelligent control approaches based on fuzzy logic have a chance to include human thinking. This paper represents modeling approach based upon operators knowledge without mathematical model of the system and optimize the controller. The experimented system is constructed for sending a ball to the goal position using wind of two DC motors in the predefined path. A vision camera to mimic human eyes detects the ball position. The system used in this experiment could be hardly modeled by mathematical methods and ould not be easily controlled by conventional manners. The controller is designed based on the input-output data and experimental knowledge obtained by trials, and optimized under the predefined performance criterion. And this paper shows the data adaptation for changeable operating condition. When the system is driven in the abnormal condition with unconsidered noise, the new optimal operating parameters could be defined by adjusting membership functions. Thus, this technique could be applied in industrial fields.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.8
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• pp.1405-1411
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• 2007

In this paper, a new speed sensorless control based on a fuzzy compensator are proposed for the interior permanent magnet synchronous motor (IPMSM) drives. The conventional proportional plus integrate(PI) control are very sensitive to step change of the command speed, parameter variations and load disturbance. To cope with these problems of the PI control, the estimated speeds are compensated by using the fuzzy logic controller (FLC). In the FLC used by the speed compensator of the IPMSM, the system control parameters are adjusted by the fuzzy rule based system, which is a logical model of the human behavior for process control. The effectiveness of algorithm is confirmed by the experiments.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1114-1115
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• 2007

In this paper, a new speed sensorless control based on a fuzzy compensator are proposed for the interior permanent magnet synchronous motor (IPMSM) drives. The conventional proportional plus integrate(PI) control are very sensitive to step change of the command speed, parameter variations and load disturbance. To cope with these problems of the PI control, the estimated speeds are compensated by using the fuzzy logic controller (FLC). In the FLC used by the speed compensator of the IPMSM, the system control parameters are adjusted by the fuzzy rule based system, which is a logical model of the human behavior for process control. The effectiveness of algorithm is confirmed by the experiments.

• The KIPS Transactions:PartB
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• v.8B no.2
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• pp.123-129
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• 2001

본 연구에서는 전문가와 운전자의 제어 지식을 더 정확하게 표현하여 퍼지 논리 제어기의 성능을 향상시킬 수 있는 소속함수 수정 알고리즘을 제안한다. 제안된 알고리즘은 제어지식을 더 정확히 표현할 수 있도록 직관적인 지식과 경험으로부터 유추된 대략적인 제어지식을 평가기준으로 하고 입출력 데이터 클러스터링에 의해 소속함수의 형태와 위치를 수정한다. 제안된 방법을 수위 조절 모델과 교통신호 제어 모델에 적용한 실험을 통해서, 제안된 알고리즘이 기존 제어기의 성능을 향상시킬 수 있고, 퍼지 제어기에서 언어적 변수에 대한 구간 설정의 어려움을 해결할 수 있음을 알 수 있었다.