• Title/Summary/Keyword: Fuzzy-logic

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A fuzzy-logic controller for a differential-drive mobile robot (이동로봇을 위한 퍼지로직 제어기)

  • 박영민;김대영;한상완;홍석교
    • 제어로봇시스템학회:학술대회논문집
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    • 1997.10a
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    • pp.532-535
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    • 1997
  • This paper describes the design of a fuzzy-logic controller for a differential-drive mobile robots. This controller uses absolute position information to modify control parameters to compensate the orientation error. CC-Control method is compensated for the internal error by wheel encoders and the fuzzy-logic control provides compensation for external errors. The validities of the proposed scheme is evaluated using simulation.

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Temeperature control method of refrigerator using fuzzy logic controller (퍼지 로직 제어기를 이용한 냉장고 온도 제어 방법)

  • 최병준;한상완;홍석교
    • 제어로봇시스템학회:학술대회논문집
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    • 1997.10a
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    • pp.28-31
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    • 1997
  • This paper describes the quick and precise controlling method for home-applied refrigerator. The proposed controller is based on the fuzzy logic control method and is designed for better performance in maintaining the constant temperature of the refrigerator. The temperature of the refrigerator is controlled by the cooling air blowing fan motor which is put on, off according to fuzzy logic controller. Finally, I study the performance of the proposed controller through the computer simulation about the approximated model of the refrigerator.

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Comparison of MPPT Based on Fuzzy Logic Controls for PMSG

  • Putri, Adinda Ihsani;Choi, Jaeho
    • Proceedings of the KIPE Conference
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    • 2011.11a
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    • pp.285-286
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    • 2011
  • Maximum Power Point Tracker (MPPT) is the big issue in generating power based on Wind Energy Conversion System. In case of unknown turbine characteristic, it is useful to implement MPPT based on fuzzy logic control. This kind of control is able to find the value of duty cycle to meet maximum power point at particular wind speed. There are many methods to develop MPPT based fuzzy logic controls. In this paper, two of the methods are compared both at low and high fluctuating wind speed.

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Multivariable control of robot manipulators using fuzzy logic (퍼지논리를 이용한 로봇 매니퓰레이터의 다변수제어)

  • 이현철;한상완;홍석교
    • 제어로봇시스템학회:학술대회논문집
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    • 1996.10b
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    • pp.490-493
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    • 1996
  • This paper presents a control scheme for the motion of a 2 DOF robot manipulator. Robot manipulators are multivariable nonlinear systems. Fuzzy logic is avaliable human-like control without complex mathematical operation and is suitable to nonlinear system control. In this paper, Implementation of fuzzy logic control of robotic manipulators shows. Algorithm has been performed with simulation packages MATRIXx and SystemBuild.

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Remote Fuzzy Logic Control of Networked Control system in Profibus-DP

  • Lee, Kyung-Chang;Lee, Suk
    • 제어로봇시스템학회:학술대회논문집
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    • 2001.10a
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    • pp.133.2-133
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    • 2001
  • This paper focuses on the feasibility of fuzzy logic control for networked control systems. In order to evaluate its feasibility, a networked control system for motor speed control is implemented on a Profibus-DP network. The NCS consists of several independent, but interacting processes running on two separate stations. By using this NCS, the network delay is analyzed to find the cause of the delay. Furthermore, in order to prove the feasibility, the fuzzy logic controllers performance is compared with those of conventional PID controllers. Based on the experimental results, the fuzzy logic controller can be a viable choice for NCS due to its robustness against parameter uncertainty.

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Vision and force/torque sensor fusion in peg-in-hole using fuzzy logic (삽입 작업에서 퍼지추론에 의한 비젼 및 힘/토오크 센서의 퓨젼)

  • 이승호;이범희;고명삼;김대원
    • 제어로봇시스템학회:학술대회논문집
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    • 1992.10a
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    • pp.780-785
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    • 1992
  • We present a multi-sensor fusion method in positioning control of a robot by using fuzzy logic. In general, the vision sensor is used in the gross motion control and the force/torque sensor is used in the fine motion control. We construct a fuzzy logic controller to combine the vision sensor data and the force/torque sensor data. Also, we apply the fuzzy logic controller to the peg-in-hole process. Simulation results uphold the theoretical results.

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Vibration Control of Flexible Nonlinear System using GA based Fuzzy Logic Controller

  • Heo, Hoon;Han, Jungyoup
    • Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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    • 1995.04a
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    • pp.142-146
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    • 1995
  • In the paper, Fuzzy Logic Controller(FLC) that determines its optimal coefficients using Genetic Algorithms is considered. It is also applied to the inverted pendulum problem known popularly as a standard plant. Flexibility of the inverted pendulum has been taken into account. In the results, Fuzzy Logic Controller under consideration successfully controls both rigid mode and flexible mode. The rule base of Fuzzy Logic Controller is automatically tuned using not only trial-error method but also Genetic Algorithms.

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Fuzzy Logic and Worldviews (퍼지논리와 세계관)

  • Park, Chang-Kyun
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2008.04a
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    • pp.333-334
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    • 2008
  • All theories are based on philosophical presupposition. Fuzzy logic is no exception. This paper alms through historical approach to show that fuzzy logic reflects relativistic and pluralistic worldviews.

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Power Factor Control of a Doubly Fed Induction Machine using Fuzzy Logic (퍼지로직을 이용한 이중여자 유도기의 역률제어)

  • Kim Jae-Hong;Kim Eel-Hwan
    • Proceedings of the KIPE Conference
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    • 2001.07a
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    • pp.268-271
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    • 2001
  • This paper describes the power factor control of doubly fed induction machine using fuzzy logic algorithm in wind power generation system. Under fuzzy logic control, which enables superior dynamic performance, the power factor is independently controllable by decoupled d, q rotor experimental results are presented.

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Cancer Cell Recognition by Fuzzy Logic

  • Na, Cheol-Hun
    • Journal of information and communication convergence engineering
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    • v.9 no.4
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    • pp.466-470
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    • 2011
  • This paper proposes the new method based on fuzzy logic which recognizes between normal and abnormal. The object image was the Thyroid Gland cell image that was diagnosed as normal and abnormal(two types of abnormal : follicular neoplastic cell, and papillary neoplastic cell), respectively. The nuclei were successfully diagnosed as normal and abnormal. The multiple feature parameters (pre-obtained 16 feature parameters of image data) were used to extract the features of each nucleus. As a consequence of using fuzzy logic algorithm, proposed in this paper, average recognition rate of 98.25% was obtained.