• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.509-511
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• 1998

Design of fuzzy logic controllers encounters difficulties in the selection of optimized membership function and fuzzy rule base, which is traditionally achieved by tedious trial-and-error process. In this paper We proposed a new method to generate fuzzy logic controllers throught genetic algorithm(GA). The controller design space is coded in base-7 strings chromosomes, where each bit gene matches the 7 discrete fuzzy value. The developed approach is subsequently applied to the design of proportional plus integral type fuzzy controller for a do-servo motor control system. It was presented in discrete fuzzy linguistic value, and used a membership function with Gaussian curve. The performance of this control system is demonstrated higher than that of a conventional PID controller and fuzzy logic controller(FLC).

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.6
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• pp.389-394
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• 2004

This paper proposes a speed estimation method using FLC(Fuzzy Logic Controller) in order to realize the speed control of PMAM(Permanent Magnet AC Motor) with no speed sensor. This method uses FLC as a adaptive laws of MRAS(Model Reference Adaptive System) and estimates the rotor speed of PMAM with a difference between the reference model and the adjustable model. Speed control is performed by PI controller with the estimated speed. The experiment is executed to verify the propriety and the effectiveness of the proposed system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.10a
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• pp.143-153
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• 1997

At the on-line control method FLC(Fuzzy Logic Controller) is stronger to the disturbance than a classical controller and its overshoot of the initialized value is excellent. The fuzzy controller can do a proper control, though it doesn't know the mathematical model of the system or the parameter value. But to make the control rule of the fuzzy controller through an expert's experiance has a changes of the control system, the control rule is fixed, it can't adjust to the environment changes of the control system, the controller output value has a minute error and it can't convergence correctly to the desired value[1][2]. There are many ways to eliminate the minute error[3][4][5], but in this paper suggests EP-FNNIC(Fuzzy Neurla Network Intelligence Controller) intelligence controller which combines FLC with NN(Neural Network) and EP(Evolution Programming). The output characteristics of EP-FNNIC controller will be compared and analyzed with FLC. It will be showed that this EP-FN IC controller converge correctly to the desirable value without any error. The convergence speed, overshoot, rising time, error of steady state of controller of these two kinds also will be compared.

• The Transactions of the Korean Institute of Power Electronics
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• v.4 no.6
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• pp.531-538
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• 1999

This paper proposes a fuzzy logic controller(FLC) for DC-DC converters in order to obtain good l perfonnances that can not be achieved by linear control tc'Chniques in the presence of wide parameter v variations. 'While the standard controller uses error and derivative of e$\pi$or, the proposed controller uses state v variables. Such method is ve$\pi$ efficient in case of DC-DC converters and can guarantee both stable s small-signal responses and improved large signal responses. The presented approach method is general and c can be applied to any dc-dc converter topologies. Through the simulations of booster, we verify the pro[Xlsed C control tc'Chnique can give a satisfactory perfonnance.

• 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.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.1 no.1
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• pp.44-49
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• 2001

For minimum phase systems, the conventional fuzzy logic controllers (FLCs) use the error and the change-of-error as fuzzy input variables. Then the control rule table is a skew symmetric type, that is, it has UNLP (Upper Negative and Lower Positive) or UPLN property. This property allowed to design a single-input FLC (SFLC) that has many advantages. But its control parameters are not automatically adjusted to the situation of the controlled plant. That is, the adaptability is still deficient. We here design a single-input direct adaptive FLC (SDAFLC). In the AFLC, some parameters of the membership functions characterizing the linguistic terms of the fuzzy rules are adjusted by an adaptive law. The SDAFLC is designed by a stable error dynamics. We prove that its closed-loop system is globally stable in the sense that all signals involved are bounded and its tracking error converges to zero asymptotically. We perform computer simulations using a nonlinear plant and compare the control performance between the SFLC and the SDAFLC.

• Korean Journal of Food Science and Technology
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• v.27 no.2
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• pp.164-169
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• 1995

Fictitious experiment to control extrusion process was carried out using the fuzzy theory. Algorithm of the fuzzy logic controller(FLC) was made based on the general principles of extrusion. In the simulation, at first, thickness of extrudate was measured as feedback input variable. Secondly, a set point of screw speed was determined as output variable of extruder operating condition through FLC. Finally, the thickness of extrudate was controlled as a given set point. Barrel heater was simply controlled as on/off state, which was not fuzzy controlled.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.3
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• pp.249-256
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• 2009

The widely used control theory based design of PI family controllers fails to perform satisfactorily under parameter variation, nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of learning through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive learning mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control (FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN, FLC and ANN controller.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1487-1490
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• 2002

In this paper, a design methodology of fuzzy sliding mode control scheme for a hydraulic elevator controlled by inverter is presented. The proposed scheme uses a fuzzy sliding mode controller(FSMC), which is designed based on the similarity between the fuzzy logic control(FLC) and the sliding mode control(SMC). The proposed method has advantages that the stability and the robustness of the FLC are proved and ensured by the sliding mode control law, and the computation burden could be reduced greatly. The validity and the effectiveness of the proposed control method have been shown through the real world industrial application results.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.897-899
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• 1999

This paper presents a scaling factor tuning method to improve the performance of fuzzy logic controller. Tuning rules and reasoning are utilized off-line to determine the scaling factors based on absolute value of the error and its difference. In this paper We proposed a new method to generate fuzzy logic controllers throught genetic algorithm. The developed approach is subsequently applied to the design of proportional plus integral type fuzzy controller for a dc-servo motor control system. The performance of this control system is demonstrated higher than a conventional fuzzy logic controller(FLC).