• International Journal of Fuzzy Logic and Intelligent Systems
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• 제12권3호
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• pp.238-244
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• 2012

A modeling of vision based robot formation control system using fuzzy logic controller and extended Kalman filter is presented in this paper. The main problems affecting formation controls using fuzzy logic controller and vision based robots are: a robot's position in a formation need to be maintained, how to develop the membership function in order to obtain the optimal fuzzy system control that has the ability to do the formation control and the noise coming from camera process changes the position of references view. In order to handle these problems, we propose a fuzzy logic controller system equipped with a dynamic output membership function that controls the speed of the robot wheels to handle the maintenance position in formation. The output membership function changes over time based on changes in input at time t-1 to t. The noises appearing in image processing change the virtual target point positions are handled by Extended Kalman filter. The virtual target positions are established in order to define the formations. The virtual target point positions can be changed at any time in accordance with the desired formation. These algorithms have been validated through simulation. The simulations confirm that the follower robots reach their target point in a short time and are able to maintain their position in the formation although the noises change the target point positions.

• 대한전기학회논문지:전력기술부문A
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• 제48권2호
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• pp.97-106
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• 1999

This paper proposes the structure and the algorithm of the Fuzzy-Neural Controller(FNNC) which is able to adapt itself to unknown plant and the change of circumstances at the Fuzzy Logic Controller(FLC) with the Neural Network. This Learning Fuzzy Logic Controller is made up of Fuzzy Logic controller in charge of a main role and Neural Network of an adaptation in variable circumstances. This construct optimal fuzzy controller applied to the 2-area load frequency control of power system, and then it would examine fitness about parameter variation of plant or variation of circumstances. And it proposes the optimal Scale factor method wsint three preformance functions( E, , U) of system dynamics of load frequency control with error back-propagation learning algorithm. Applying the controller to the model of load frequency control, it is shown that the FNNC method has better rapidity for load disturbance, reduces load frequency maximum deviation and tie line power flow deviation and minimizes reaching and settling time compared to the Optimal Fuzzy Logic Controller(OFLC) and the Optimal Control for optimzation of performance index in past control techniques.

• 한국통신학회논문지
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• 제19권11호
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• pp.2189-2198
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• 1994

본 논문에서는 퍼지 제어 시스템에 사용되는 퍼지 논리 제어기의 성능을 향상시키기 위한 여러가지 알고리즘들 중에서 학습기법에 속하는 퍼지 메타 규칙에 기초한 자기 동조 기법을 사용하여 직류 서보 전동기 제어를 위한 자기 동조 퍼지 논리 제어기를 구현해서, 자기 동조 퍼지 논리 제어기의 설계와 시뮬레이션 및 실험 결과를 고찰하고, 그 결과를 일반적인 퍼지 논리 제어기의 결과와 비교하여 자기 동조 퍼지 논리 제어기의 성능을 평가한다.

• 제어로봇시스템학회논문지
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• 제10권2호
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• pp.125-130
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• 2004

This paper describes an approach for synthesizing a Fuzzy Logic Controller(FLC) that combines the benefits of fuzzy logic control and fuzzy logic gain scheduling for the F/A-18 aircraft. Specially, fuzzy rules are utilized on-line to determine the denoralization factor(Κ) of a feedback fuzzy controller based on the dynamic pressure(Q) indicateing the region of the flight envelop the aircraft is operating in. Simulation results demonstrate that the proposed FLC provides excellent compensation for time-varying and/or nonlinear characteristics of the aircraft, and that it also exhibits satisfactory robustness with noisy air data sensors.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2011년도 추계학술대회
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• pp.34-36
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• 2011

In recent years, shipping at the sea has been rapidly grown in marine nations and vessel's collisions are increasing as well. The collision avoidance is one of issues maritime safety. To reduce vessels' collisions, the fuzzy inference system is one of popular algorithms for collision avoidance. In this paper we aim to implement Matlab. Fuzzy logic toolbox software for collision avoidance algorithm. For this we used an original Matlab fuzzy logic toolbox and customized the toolbox for the collision avoidance algorithm.

• 대한기계학회논문집A
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• 제25권9호
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• pp.1468-1474
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• 2001

In this paper, a new fuzzy-logic anti-swing control scheme is proposed for a three-dimensional overhead crane. The proposed control consists of a position servo control and a fuzzy-logic control. The position servo control is used to control the trolley position and rope length, and the fuzzy-logic control is used to suppress load swing. The proposed control guarantees not only prompt suppression of load swing but also accurate control of trolley position and rope length for the simultaneous travel, traverse, and hoisting motions of the crane. The effectiveness of the proposed control is shown by experiments with a prototype three-dimensional overhead crane.

• 정보관리연구
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• 제24권3호
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• pp.73-100
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• 1993

본 연구(硏究)는 불 정보 시스템의 단점을 보완하는 방법으로 퍼지 논리(論理)를 이용한 정보검색시스템을 설계하였다. 퍼지 정보검색 시스템은 질의어(質疑語)와 문헌들을 표현하는 각 용어(用語)들에 가중치(加重値)를 부여하고, 이것을 바탕으로 질의어에 대한 각 문헌들의 적합도(適合度)를 결정하는 것이다. 본 연구에서는 기존의 연구들을 비교 분석한 후, 효과적인 모델을 제시하고 시스템 성능을 평가하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 추계학술대회 논문집 학회본부 B
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• pp.493-495
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• 1998

Robust design is one method to make manufacturing less sensitive to manufacturing process. Also it is cost effective technique to improve the quality process. This method uses statistically planned experiments to vary settings of important process control parameters. In this paper we apply fuzzy optimization and fuzzy logic system to robust design concept. First a method which uses fuzzy optimization in obtaining optimum settings by measured data from experiments will be presented. Second, fuzzy logic system is made to reduce experiments using experiments results consisted with key control parameter combinations. Then optimum parameter set points are obtained by the descrebed first fuzzy optimization method after prediction the results of each parameter combinations considering each control parameter variations by nonsingleton fuzzy logic system concept.

• 한국지능시스템학회논문지
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• 제9권4호
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• pp.384-388
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• 1999

This paper describes an approach for synthesizing a modularized gain scheduled PD type fuzzy logic controller(FLC) of nonlinear magnetic bearing system where the gains of FLC are on-line adapted according to the operating point. Specifically the systematic procedure via root locus technique is carried out for the selection of the gains of FLC. Simulation results demonstrate that the proposed gain scheduled fuzzy logic controller yields not only maximization of stability boundary but also better control performance than a single operating point (without gain scheduling)fuzzy controller.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제15권4호
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• pp.217-231
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• 2015

A novel fuzzy learning framework that employs fuzzy inference to solve the problem of Multiple Instance Learning (MIL) is presented. The framework introduces a new class of fuzzy inference systems called Multiple Instance Mamdani Fuzzy Inference Systems (MI-Mamdani). In multiple instance problems, the training data is ambiguously labeled. Instances are grouped into bags, labels of bags are known but not those of individual instances. MIL deals with learning a classifier at the bag level. Over the years, many solutions to this problem have been proposed. However, no MIL formulation employing fuzzy inference exists in the literature. Fuzzy logic is powerful at modeling knowledge uncertainty and measurements imprecision. It is one of the best frameworks to model vagueness. However, in addition to uncertainty and imprecision, there is a third vagueness concept that fuzzy logic does not address quiet well, yet. This vagueness concept is due to the ambiguity that arises when the data have multiple forms of expression, this is the case for multiple instance problems. In this paper, we introduce multiple instance fuzzy logic that enables fuzzy reasoning with bags of instances. Accordingly, a MI-Mamdani that extends the standard Mamdani inference system to compute with multiple instances is introduced. The proposed framework is tested and validated using a synthetic dataset suitable for MIL problems. Additionally, we apply the proposed multiple instance inference to fuse the output of multiple discrimination algorithms for the purpose of landmine detection using Ground Penetrating Radar.