• Journal of the Korea Computer Industry Society
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• v.3 no.1
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• pp.95-104
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• 2002

This paper presents some studies on the Internet TCP/IP(Transmission Control Protocol-Internet Protocol) traffic over ATM(Asynchronous Transfer Mode) UBR(Unspecified Bit Rate) and ABR(Available Bit Rate) classes of service. Fuzzy logic prediction has been used to improve the efficiency and fairness of traffic throughput. For TCP/IP over UBR, a novel fuzzy logic based cell dropping scheme is presented. This is referred to as fuzzy logic selective cell drop (FSCD). A key feature of the scheme is its ability to accept or drop a new incoming packet dynamically based on the predicted future buffer condition in the switch. This is achieved by using fuzzy logic prediction for the production of a drop factor. Packet dropping decision is then based on this drop factor and a predefined threshold value. Simulation results show that the proposed scheme significantly improves TCP/IP efficiency and fairness. To study TCP/IP over ABR, we applied the fuzzy logic ABR service buffer management scheme from our previous work to both approximate and exact fair rate computation ER(Explicit cell Rate) switch algorithms. We then compared the performance of the fuzzy logic control with conventional schemes. Simulation results show that on zero TCP packet loss, the fuzzy logic control scheme achieves maximum efficiency and perfect fairness with a smaller buffer size. When mixed with VBR traffic, the fuzzy logic control scheme achieves higher efficiency with lower cell loss.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1856-1859
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• 1997

This paper describes the arousal measurement and the control system using fuzzy logic to prevent drowsy driving. Sugeno's method was used for fuzzy inference in this study. Membership function and rule base were determined form the modfied arousal level criteria. The output of fuzzy inference tracked well the change of subject's arousal level. When IRI(Inter-SIR interval) was under the 60sec, maximum output of three step warning method was medium sound, but that of fuzzy logic system was changed from medium to big. Furthermore, the output of the fuzzy inference was highly correlated with $N_{z}$(r=0.99). Therefore, the fuzzy inference method for evaluation and the control of arousal will be more effective at real driving sityation than three step warning method.ning method.

• Proceedings of the KIPE Conference
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• 2008.06a
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• pp.127-129
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• 2008

This paper describes a method of approximate reasoning for fuzzy control of servo system, based on decomposition of -level fuzzy sets. It is propose that logic circuits for fuzzy PI+PD are a body from fuzzy inference to defuzzificaion in cases where the output variable u directly is generated PWM. The effectiveness for robust and faster response of the fuzzy control scheme is verified for a variable parameter by comparison with a PID control and fuzzy control. A position control of DC servo system with a fuzzy logic controller successfully demonstrated.

• Proceedings of the IEEK Conference
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• 1998.06a
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• pp.897-900
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• 1998

Through fuzzy logic controller is very useful to many areas, it is difficult to build up the rule-base by experience and trial-error. So, effective self-tuning fuzzy controller for the position control of ball and beam is designed. In this paper, we developed the neural network control system with fuzzy-neuron which conducts the adjustment process for the parameters to satisfy have nonlinear property of the ball and beam system. The proposed algorithm is based on a fuzzy logic control system using a neural network learinign algorithm which is a back-propagation algorithm. This system learn membership functions with input variables. The purpose of the design is to control the position of the ball along the track by manipulating the angualr position of the serve. As a result, it is concluded that the neural network control system with fuzzy-neuron is more effective than the conventional fuzzy system.

• Proceedings of the KIEE Conference
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• 2002.04a
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• pp.79-81
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• 2002

This paper develops a adaptive fuzzy controller based fuzzy logic control for high performance speed controller in permanent magnet synchronous motor(PMSM) drives. In the proposed system, fuzzy control is used to implement the direct controller as well as the adaptation mechanism. The operation of the direct fuzzy controller and the fuzzy logic based adaptation mechanism is studied. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive fuzzy controller is evaluated by simulation for various operating conditions. The validity of the proposed adaptive fuzzy controller is confirmed by performance results for PMSM drive system.

• The Journal of the Acoustical Society of Korea
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• v.17 no.1E
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• pp.36-43
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• 1998

A fuzzy logic filter is constructed from a set of fuzzy IF-THEN rules which change adaptively to minimize some criterion function as new information becomes available. This paper generalizes the fuzzy logic filter and it's adaptive filtering algorithm to include complex parameters and complex signals. Using the complex Stone-Weierstrass theorem, we prove that linear combinations of the fuzzy basis functions are capable of uniformly approximating and complex continuous function on a compact set to arbitrary accuracy. Based on the fuzzy basis function representations, a complex orthogonal least-squares (COLS) learning algorithm is developed for designing fuzzy systems based on given input-output pairs. Also, we propose an adaptive algorithm based on LMS which adjust simultaneously filter parameters and the parameter of the membership function which characterize the fuzzy concepts in the IF-THEN rules. The modeling of a nonlinear communications channel based on a complex fuzzy is used to demonstrate the effectiveness of these algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.486-489
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• 1996

The objective of this paper is to design a force feedback controller for bilateral control of a master-slave manipulator system. In a bilateral control system, the motion of the master device is followed by the slave one, while the force applied to the slave is reflected on the master. In this paper, a fuzzy logic controllers applied to the system. Using the fuzzy logic controller, the knowledge of the system dynamics is not needed. Simulations and experimental results show the performance of the proposed controller.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.141.1-141
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• 2001

This paper describes an approach for synthesizing a modularized gain scheduled PD type fuzzy logic controller(FLC) for a high maneuverable aircraft system, where the gains of FLC are on-line adapted according to the flight condition. Specially, the systematic procedure via root locus technique is carried out for the sellection of the gains of FLC. Simulation results demonstrate that the proposed gain scheduled fuzzy logic controller yields better control performance than the normal (without gain scheduling) fuzzy controller.

• Proceedings of the IEEK Conference
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• 2000.06e
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• pp.43-46
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• 2000

In this paper, we present a method for vehicle tracking systems using $\alpha$-$\beta$ filter based on fuzzy logic. The $\alpha$-$\beta$ filter estimates the future target positions using fixed $\alpha$.$\beta$ coefficients. We utilize the fuzzy logic to make $\alpha$ and $\beta$ coefficients very with the position. Comparisons of tracking performance made for three different schemes: the $\alpha$-$\beta$ filter, $\alpha$-$\beta$filter using fuzzy logic, and the kalman filter.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.6
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• pp.625-632
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• 1992

This paper presents a new dc-motor position control approached by Variable Structure System. In order to eliminate a steady-state position error, we propose a switching function composed of position error, velocity, and current ripple. The switching function has an advantage compared to other ones. To determine the control signal voltage, we use a fuzzy logic method. The simulation results show expected performances.