• 융합신호처리학회 학술대회논문집
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• 한국신호처리시스템학회 2000년도 추계종합학술대회논문집
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• pp.221-224
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• 2000

We consider one of robust controller, fuzzy-sliding mode controller dealing with model uncertainty, simplified representation of nonlinear system, changed parameters of plant. We propose fuzzy-sliding mode algorithm which provides control input that has system states approaching the choosed sliding surface. This fuzzy controller has a rule base to get initial states converged on sliding surface. This algorithm Is applied to a transfer function of DC motor to be modeled simply and do position control of DC motor due to system parameters. We compare fuzzy-sliding mode controller to both sliding mode controller and fuzzy controller to identify roust control.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 추계 학술대회 학술발표 논문집
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• pp.95-98
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• 2003

In this paper, an adaptive fuzzy neural control of unknown nonlinear systems based on the rapid learning algorithm is proposed for optimal parameterization. We combine the advantages of fuzzy control and neural network techniques to develop an adaptive fuzzy control system for updating nonlinear parameters of controller. The Fuzzy Neural Network(FNN), which is constructed by an equivalent four-layer connectionist network, is able to learn to control a process by updating the membership functions. The free parameters of the AFN controller are adjusted on-line according to the control law and adaptive law for the purpose of controlling the plant track a given trajectory and it's initial values are off-line preprocessing, In order to improve the convergence of the learning process, we propose a rapid learning algorithm which combines the error back-propagation algorithm with Aitken's $\delta$$\^$2/ algorithm. The heart of this approach ls to reduce the computational burden during the FNN learning process and to improve convergence speed. The simulation results for nonlinear plant demonstrate the control effectiveness of the proposed system for optimal parameterization.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1996년도 추계학술발표회논문집(1)
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• pp.85-90
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• 1996

In this paper, the self-organizing fuzzy logic controller is developed for water level control of steam generator. In comparison with conventional fuzzy logic controllers, this controller performs control task with no control rules at initial and creates control rules as control behavior goes on, and also modifies its control structure when uncertain disturbance is suspected. Selected parameters in the fuzzy logic controller are updated on-line by the gradient descent loaming algorithm based on the performance cost function. This control algorithm is applied to water level control of steam generator model developed by Lee, et al. The computer simulation results confirm good performance of this control algorithm in all power ranges. This control algorithm can be expected to be used for automatic control of feedwater control system in the nuclear power plant with digital instrumentation and control systems.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2004년도 춘계학술대회 학술발표 논문집 제14권 제1호
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• pp.454-457
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• 2004

In this paper, control method proposed for effective speed control of the induction motor indirect vector control. For the induction motor drive, indirect vector control scheme that controls torque current and flux current of the stator current independently so that it can have improved dynamics. Also, neuro-fuzzy algorithm employed for torque current control in order to optimal speed control The proposed neuro-fuzzy algorithm can be applied to the precise speed control of an induction motor drive system or the field of any other power systems.

• 한국정보통신학회논문지
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• 제3권2호
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• pp.363-371
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• 1999

본 논문에서는 유전자 알고리즘을 이용한 최적의 퍼지 제어기 설계에 대한 방식을 제안한다. 제안하는 방식은 최적화 문제에 매우 효과적인 유전자 알고리즘을 이용하여 퍼지 제어기의 퍼지규칙, 입ㆍ출력 스케일링 펙터를 결정하는 방식이다. 서보 시스템에 적합한 퍼지 규칙은 퍼지 제어기의 성능지표인 적합도 함수를 사용한다. 제안된 제어 방식의 유용성을 확인하기 위하여 단일 링크 매니퓰레이터를 제어 대상으로 시뮬레이션 및 실험을 하여 일반적인 퍼지 제어 방식과 제어 성능 및 특성을 비교 검토한다.

• 한국경영과학회지
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• 제18권2호
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• pp.23-43
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• 1993

This paper employs fuzzy variables instead of deterministic variables for job times in a project network. A fuzzy variable has its value restricted by a possibility distribution. This paper utilizes the triangular possibility distribution which has three estimated times. That is normal, resonable, and crash job times. This paper develops a fuzzy k-longest path algorithm, by utilizing the k-longest path algorithm. This algorithm will be useful to control the project the project network by considering the project completion possibility.

• 설비공학논문집
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• 제18권2호
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• pp.163-171
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• 2006

In order to control multi-zone temperatures, a multi-type air-conditioning system may be used. In this study, control algorithms for the compressor and the electronic expansion valve of a multi-type air-conditioning system were developed by using fuzzy logics. The compressor control algorithm was composed of a compressor pressure setpoint algorithm, a compressor pressure setpoint reset algorithm, and a compressor frequency setpoint algorithm. The electronic expansion valve control algorithm was composed of an indoor temperature control algorithm, and a superheat control algorithm. These algorithms were applied to a multi-type air-conditioning system. Test showed good results for the control of a multi-type air-conditioning system.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 학술회의 논문집 정보 및 제어부문 A
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• pp.255-258
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• 2003

It is hard to centrol the wheeled mobile robot because of uncertainty of modeling, non-holonomic constraint and so on. To solve the problems, we design the controller of wheeled mobile robot based on fuzzy-neural network algorithm. In this paper, we should research the problem of classical controller for path-tracking algorithm and design of Fuzzy-Neural Network algorithm controller. Classical controller acquired different control value according to change of initial position and direction. In this control value having very difficult and having acquired a lot of trial and error Fuzzy is implemented to adaptive adjust control value by error and change of error and neural network is implemented to adaptive adjust the control gain during the optimization. The computer simulation shows that the proposed fuzzy-neural network controller is effective.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.539-542
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• 2005

This paper contains studies which are Cruise speed control which is made by PID algorithm and automated steering system for avoiding the obstacle coming from the front which is using Fuzzy algorithm. This mobile car uses DC motor whose speed is detected by encoder. Ultrasonic Waves Sensor established in the front detects the obstacle and the curve. And the sensor established in the side detects the distance of the space of the road. If the sensor detects the obstacle or the curve, the car is controlled by using Fuzzy algorithm. The Fuzzy algorithm calculates the speed and steering angle by using the value which is obtained from sensor.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1991년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 22-24 Oct. 1991
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• pp.1127-1132
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• 1991

Pneumatic control system has been used mainly for endpoint position control because of the compressibility, viscosity and low output stiffness of air which causes nonlinear flow characteristics. In this paper, pneumatic position control algorithms using fuzzy rule were developed to achieve faster and more stable response than conventional PI control algorithm. The performances of the proposed algorithms were compared by computer simulations with them of PI controller. From those simulations it was shown that the proposed algorithms are more efficient about settling time, steady state error and overshoot than PI control algorithm.