• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.1
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• pp.183-187
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• 2007

This paper proposes Fuzzy Speed Estimator using Fuzzy Logic Controller(FLC) as a adaptive law in Model Reference Adaptive System(MRAS) in order to realize the speed-sensorless control of an induction motor. Fuzzy Speed Estimator estimates the speed of an induction motor with a rotor flux of the reference model and the 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 of the rotor flux as the input of FLC. The experiment is executed to verify the propriety and the effectiveness of the proposed speed estimator.

• Journal of Power System Engineering
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• v.22 no.1
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• pp.18-24
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• 2018

This paper focuses on systematic design about the membership ranges of the main design factors such as control error, control error rate, and sampling time for the fuzzy logic control of the variable speed drive refrigeration cycle. The upper and the lowest limit of the membership ranges are set up from the data of static characteristics obtained by experiments. Three kinds of membership ranges on the control error and the control error rate are tested by experiments. Especially, an effect of sampling time on control performance is also investigated in the same way. Experimental data showed the control error rate and the sampling time strongly effected on the control performance of the refrigeration cycle with a variable speed drive.

• Transactions of the Korean Society of Automotive Engineers
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• v.5 no.5
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• pp.37-50
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• 1997

The objective of this paper is to study on the idle speed control using the fuzzy logic controller under load disturbance. The design procedure for fuzzy logic controller depends on the expert's knowledge or trial and error. The inputs of the fuzzy controller are error of rpm and variation of rpm. The output of the fuzzy controller is an ISC motor step and ignition timing. The airflow is controlled by the ISC motor movement and the idle speed is controlled by the airflow control and ignition timing control. During the control, air to fuel was checked by LAMBDA sensor. All experiments were performed in a real vehicle.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.244-249
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• 1998

Abstract-A speed controller of a induction motor using Microcontroller and Fuzzy logic is presented in the paper. Generally fuzzy logic controller is known as a controller which 춤 be coped with a non-linear and a complex system. A fuzzy logic is used for robust and fast speed control and space vector modulation method is used for PWM wave generation in this proposed system. The results of experiment show excellence of the proposed system and that the proposed system is appropriate to control the speed of a induction motor for industrial application

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.49-53
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• 2008

A fuzzy inference logic system is proposed for gas turbine engine fault isolation. The gas path measurements used for fault isolation are exhaust gas temperature, low and high rotor speed, and fuel flow. The fuzzy inference logic uses rules developed from a model of performance influence coefficients to isolate engine faults while accounting for uncertainty in gas path measurements. Inputs to the fuzzy inference logic system are measurement deviations of gas path parameters which are transferred directly from the ECM(Engine Control Monitoring) program and outputs are engine module faults. The proposed fuzzy inference logic system is tested using simulated data developed from the ECM trend plot reports and the results show that the proposed fuzzy inference logic system isolates module faults with high accuracy rate in the environment of high level of uncertainty.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.1264-1272
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• 1993

The software for unmanned control of three row typed rice combine has been developed using fuzzy logic. Three fuzzy variables were used : operating status of combine, steering, and speed. Eleven fuzzy rules were constructed and the eleven linguistic variables were used for the fuzzy rules. Six sensors were use of to get input values and sensor input values were quantified into 11 levels. The fuzzy output was infered with fuzzy inferrence which uses the correlation product encoding , and it must have been defuzzified by the method of center of gravity to use it for the control. The result of performance test using graphic simulation showed that the intelligently unmanned control of a rice combine was possible using fuzzy logic control.

• Journal of Power Electronics
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• v.15 no.3
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• pp.730-740
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• 2015

This paper presents model reference adaptive system speed estimators based on Type-1 and Type-2 fuzzy logic controllers for the speed sensorless direct torque and flux control of an induction motor drive (IMD) using space vector pulse width modulation. A Type-1 fuzzy logic controller (T1FLC) based adaptation mechanism scheme is initially presented to achieve high performance sensorless drive in both transient as well as in steady-state conditions. However, the Type-1 fuzzy sets are certain and cannot work effectively when a higher degree of uncertainties occurs in the system, which can be caused by sudden changes in speed or different load disturbances and, process noise. Therefore, a new Type-2 FLC (T2FLC) - based adaptation mechanism scheme is proposed to better handle the higher degree of uncertainties, improve the performance, and is also robust to different load torque and sudden changes in speed conditions. The detailed performance of different adaptation mechanism schemes are performed in a MATLAB/Simulink environment with a speed sensor and sensorless modes of operation when an IMD is operates under different operating conditions, such as no-load, load, and sudden changes in speed. To validate the different control approaches, the system is also implemented on a real-time system, and adequate results are reported for its validation.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.11
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• pp.753-759
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• 2004

This paper presents a modeling and simulation of a fuzzy controller for maximum power extraction of a grid-connected wind energy conversion system with a link of a rectifier and an inverter. It discusses the maximum power control algorithm for a wind turbine and proposes, in a graphical form, the relationships of wind turbine output, rotor speed, power coefficient, tip-speed ratio with wind speed when the wind turbine is operated under the maximum power control. The control objective is to always extract maximum power from wind and transfer the power to the utility by controlling both the pitch angle of the wind turbine blades and the inverter firing angle. Pitch control method is mechanically complicated, but the control performance is better than that of the stall regulation method. The simulation results performed on MATLAB will show the variation of generator's rotor angle and rotor speed, pitch angle, and generator output.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.138-138
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• 2000

In this paper, a methodology for designing a controller based on inverse dynamics for speed control of DC motors is presented. The proposed controller consists of a prefilter, the inverse dynamic model of a system and a fuzzy logic controller. The prefilter prevents high frequency effects from the inverse dynamic model. The model of the system is characterized by a nonlinear equation with coulomb friction. The fuzzy logic controller regulates the error between the set-point and the system output which may be caused by disturbances and it simultaneously traces the change o( the reference input. The parameters of the model are estimated by a genetic a]gorithm. An experimental work on a DC motor system is carried out to illustrate the performance of the proposed controller

• Proceedings of the KIEE Conference
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• 2001.07b
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• pp.1147-1150
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• 2001

This paper represents a realization of a fuzzy PI control method for a speed control of BLDC motor. In other words, the gains of the PI controller is tuned by a fuzzy logic controller. Simplified reasoning methods are used for fuzzy reasoning. Fuzzy logic speed controller is designed by using the high performance of DSPchip(TMS320F240). By experiment, it is confirmed that the speed of BLDC motor well follows an command speed in the load variables or speed variables.