• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.15 no.3
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• pp.57-66
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• 2001

This paper resents a control approach for designing a fuzzy-PI controller for a synchronous generator excitation and SVC system A combination of thyristor-controlled reactors and fixed capacitors (TCR-FC) type SVC is recognized as having the must fiexible control and high speed response, which has been widely utilized in power systems, is considered and designed to improve the response of a synchronous generator, as well as controlling the system voltage A Fuzzy-PI controller for SVC system was proposed in this paper. The PI gain parameters of the proposed Fuzzy-PI controller which is a special type of PI ones are self-tuned by fuzzy inference technique. It is natural that the fuzzy inference technique should be barred on humans intuitions and empirical knowledge. Nonetheless, the conventional ones were not so. Therefore, In this paper, the fuzzy inference technique of PI gains using MMGM(Min Max Gravity Method) which is very similar to humans inference procedures, was presented and allied to the SVC system. The system dynamic responses are examined after applying all small disturbance condition.

• Journal of Advanced Navigation Technology
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• v.15 no.3
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• pp.449-455
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• 2011

In this paper, Proposed controller prevent compressor surge and reduce the acceleration time of the fuel flow control system for turbo-jet engine. Turbo-jet engine controller is designed by applying fuzzy PI+D control algorithm and make an inference by applying Mamdani's inference method and the defuzzification using the center of gravity method. Fuzzy inference results are used as the fuel flow control inputs to prevent compressor surge and flame-out for turbo-jet engine and the controller is designed to converge to the desired speed quickly and safely. Using MATLAB to perform computer simulations verified the performance of the proposed controller.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.5
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• pp.1050-1057
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• 2004

The purpose of study is development of a fuzzy controller which independent of a computer and its software for fuzzy control of servo system. This paper describes a method of approximate reasoning for fuzzy control of servo system, based on decomposition of $\alpha$-level fuzzy sets, It is propose that fuzzy logic algorithm is a body from fuzzy inference to defuzzificaion in cases where the output variable u directly is generated PWM. The effectiveness of quantified $\alpha$-levels on input/output characteristics of fuzzy controller and output response of DC servo system is investigated. It is concluded that $\alpha$-cut 4 levels give a sufficient result for fuzzy control performance of DC servo system. The experimental results shows that the proposed hardware method is effective for practical applications of DC servo system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.05a
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• pp.207-210
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• 2002

본 논문에서는 인간과 MR(Mobile Robot)이 일정한 거리를 유지하면서 인간을 추종할 수 있도록 퍼지 제어기를 이용한 지능적 추론 방법을 제안하였다. 로봇은 다중 초음파 센서와 PC 카메라를 사용하여 인간과 로봇의 거리와 위치를 인지하고 로봇의 진행 방향과 속도를 퍼지 추론하는 방법을 사용하였다. 먼저 초음파 센서와 카메라를 사용하여 주변 환경에 대한 정보를 획득하고 주변환경을 표현하는 것이 중요하다. 센서와 카메라에 의해 입수된 정보로부터 로봇을 제어할 수 있도록 속도와 방향을 이용하여 추론하고 로봇을 제어하였다. 논문에서 제안된 퍼지 로직 알고리듬의 유용성을 검증하기 위해 실제 Mobile Robot을 이용한 주행실험을 반복 시행하여 요구된 결과를 얻음으로써 퍼지로직 제어기의 우수성을 확인할 수 있었다.

• Journal of the Korean Institute of Navigation
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• v.23 no.4
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• pp.97-104
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• 1999

본 논문은 퍼지신경망을 이용한 유연성 단일 링크 로봇 매니퓰레이터의 위치제어에 관한 논문이다. 제안된 퍼지신경망 모델은 전건부와 결론부에 퍼지집합을 갖는 퍼지규칙으로 구성된 퍼지모델을 표현하고, 퍼지추론을 수행하는 기능을 가진다. 유연성 로봇 매니퓰레이터에 대한 동적모델을 유도하고, 시뮬레이션을 통해 PID 제어기와 비교 분석하였다. 그 결과 제안된 제어기가 PID 제어기보다도 개선된 성능을 확인하였다.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.52-65
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• 1997

This paper presents a design technique of self tuning fuzzy controller for load frequency control of power system. The proposed parameter self tuning algorithm of fuzzy controller is based on the gradient method using four direction vectors which make error between inference values of fuzzy controller and output values of the specially selected optimal controller reduce steepestly. Using input-output data pair obtained from optimal controller, the parameters in antecedent part and in consequent part of fuzzy inference rules are learned and tuned automatically using the proposed gradient method. The related simulation results show that the proposed fuzzy controller is more powerful than the conventional ones for reductions of undershoot and steady-state load frequency deviation and for minimization of settling time.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.2
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• pp.175-181
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• 2009

In this paper, it is suggested that the selection method of parameter of Power System Stabilizer(PSS) with robustness in low frequency oscillation for Static VAR Compensator(SVC) using a self tuning fuzzy controller for a synchronous generator excitation and SVC system. The proposed parameter self tuning algorithm of fuzzy controller is based on the steepest decent method using two direction vectors which make error between inference values of fuzzy controller and output values of the specially selected PSS reduce steepestly. Using input-output data pair obtained from PSS, the parameters in antecedent part and in consequent part of fuzzy inference rules are learned and tuned automatically using the proposed steepest decent method.

• The Journal of the Acoustical Society of Korea
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• v.19 no.7
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• pp.34-41
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• 2000

This paper proposes a new double-talk detection algorithm which is based on the fuzzy rules, in the adaptive echo canceller of telecommunication system. In this method, the two inputs of the fuzzy inference for detecting double-talk condition are used. One is the cross-correlation coefficient between the error signal and the primary signal which is the summation of the real echo signal and the near-end signal. The other one is the cross-correlation coefficient between the estimation error signal and the primary signal. The fuzzy controller makes a fuzzification for two inputs by the membership functions of trapezoid does the max-min composition using if-then rules. The composed result is defuzzificated by the center gravity method. And by defuzzificated values, the double-talt the echo path variance, and the echo path variance during the double-talk are detected. It is confirmed by computer simulation that this fuzzy double-talk detector is able to estimate the double talk and the echo path variation condition, and even track echo path variation more accurately than the conventional algorithm during the double-talk period.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.10a
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• pp.447-452
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• 2004

동기전동기를 처음 기동시킬 때는 유도전동기와 같이 동작하게 된다. 회전자가 고정자 자계에 거의 도달하였을 때 M 전류를 투입하게 되면, 회전자의 여자코일에 동기화 토크가 발생하게 된다. 그러나 동기화 토크의 부족은 회전자의 첫 동요 시, 회전자 각의 불안정을 야기하게 된다. 동기화 토크는 신속 정확한 동작 제어에 의해 회복될 수 있다. 더욱이 역률 100%의 안정도로 동작하기에는 어려운 부분이 있다. 그러므로 본 논문에서는 이러한 문제를 해결하기 위해 퍼지 추론 기법을 이용한 여자 전류 제어 시스템을 제안하였다. 그 주된 원리는 다양한 부하 조건하에서 부하각과 역률 100%의 동작점을 추정하고, 퍼지 추론 기법에 의해 여자 전류를 제어하는 것이다. 제안된 퍼지 제어기는 각종 특수 동작 명령어로 사용되는 마이크로프로세서형 PLC(Programmable Logic Controller)를 사용하여 구현되었으며, 전기자 전류를 감지하는 제어전압 보상기, 비교기, 그리고 쵸퍼회로로 구성된 기존의 제어기에 비해 성능이 우수하다. 이는 일련의 실험을 통해 역률 100%에서의 개선된 안정적인 동작이 가능함을 보여주었다.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.7
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• pp.970-976
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• 2007

In this study, Polynomial Network Pattern Classifier(PNC) based on Fuzzy Inference Mechanism is designed and its parameters such as learning rate, momentum coefficient and fuzzification coefficient are optimized by means of Particle Swarm Optimization. The proposed PNC employes a partition function created by Fuzzy C-means(FCM) clustering as an activation function in hidden layer and polynomials weights between hidden layer and output layer. Using polynomials weights can help to improve the characteristic of the linear classification of basic neural networks classifier. In the viewpoint of linguistic analysis, the proposed classifier is expressed as a collection of "If-then" fuzzy rules. Namely, architecture of networks is constructed by three functional modules that are condition part, conclusion part and inference part. The condition part relates to the partition function of input space using FCM clustering. In the conclusion part, a polynomial function caries out the presentation of a partitioned local space. Lastly, the output of networks is gotten by fuzzy inference in the inference part. The proposed PNC generates a nonlinear discernment function in the output space and has the better performance of pattern classification as a classifier, because of the characteristic of polynomial based fuzzy inference of PNC.