• 대한전기학회논문지:시스템및제어부문D
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• 제50권12호
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• pp.566-574
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• 2001

In designing fuzzy models and controllers, we encounter a major difficulty in the identification f an optimized fuzzy rule base, which is traditionally achieved by a tedious trial-and-error process. This paper presents an approach to the evolutionary design of an optimal fuzzy rule base for modeling and control. Evolutionary programming is used to simultaneously evolve the structure and the parameter of fuzzy rule base for a given task. To check the effectiveness of the suggested approach, four numerical examples are examined. The performance of the identified fuzzy rule bases is demonstrated.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권2호
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• pp.90-96
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• 2005

This paper presents hardware scheme of fuzzy inference engine, based on α-level set decomposition of fuzzy sets for fuzzy control of DC servo system. We propose a method which is directly converted to PWM actuating signal by a one body of fuzzy inference and defuzzification. The influence of quantity α-levels on input/output characteristics of fuzzy controller and output response of DC servo system is investigated. It is concluded that quantity α-cut 4 give a sufficient result for fuzzy control performance of DC servo system. The experimental results shows that the proposed hardware method is effective for practical applications of DC servo system.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국내학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.436-440
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• 1992

As an approach to design the intelligent controller, this paper proposes a new FNN(Fuzzy Neural Network) control method using the hybrid combination of fuzzy logic control and neural network. The proposed FNN controller has two important capabilities, namely, adaptation and learning. These functions are performed by the following process. Firstly, identification of the parameters and estimation of the states for the unknown plant are achieved by the MNN(Model Neural Network) which is continuously trained on-line. And secondly, the learning is performed by FNN controller. The error back propagation algorithm is adopted as a learning technique. The effectiveness of the proposed method will be demonstrated by computer simulation of a two d.o.f. robot manipulator.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2473-2475
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• 2000

In this paper, we design a GA-fuzzy controller for position control and anti-swing at the destination point. Applied genetic algorithm is used to complement the demerit such as the difficulty of the component selection of fuzzy controller, namely, scaling factor, membership function and control rules. Lagrange equation is used to represent the motion equation of trolley and load in order to obtain mathematical modelling.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1996년도 하계학술대회 논문집 B
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• pp.990-992
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• 1996

The design of I/O scaling factors for fuzzy controller system is proposed in this paper. The proposed method is for nonlinear input scaling factor and variable output scaling factor. The fuzzy controller is evaluated by computer simulation on the 1st order process and 2nd order process. Simulation results showed robust characteristics for variable reference signal.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1994년도 추계학술대회 논문집 학회본부
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• pp.48-50
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• 1994

In this paper, the optimal fuzzy controller of exciter and governor in synchronous generator improve the stability of power system with varying loads and disturbances in power system. Parameters of the proposed fuzzy controller were optimally self-tuned by the steepest descent method and were applied to power system stabilization. The related simulation results show that the proposed control technique are more powerful than the conventional ones for reductions of undershoot and for minimization of settling time.

• Journal of Biosystems Engineering
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• 제28권2호
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• pp.117-126
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• 2003

This study was conducted to develop precision control system fur cold storage facility that could offer safe storage environment for green grocery. For that reason of neuro-fuzzy control system with learning ability algorithm and single chip neuro-fuzzy micro controller was developed for cold storage facility. Dynamic characteristics and hunting of neuro-fuzzy control system were far superior to on-off and fuzzy control system. Dynamic characteristics of temperature were faster than on-off control system by 1,555 seconds(123% faster) and fuzzy control system by 460 seconds(36.4% faster). When system was arrived at steady state. hunting was ${\pm}$0.5$^{\circ}C$ in on-off control system, ${\pm}$0.4$^{\circ}C$ in fuzzy control system, and ${\pm}$0.3$^{\circ}C$ in neuro-fuzzy control system. Hunting of humidity and wind velocity was also controlled precisely by 70 to 72.5% and 1m/s For storage experiment with onion, characteristics of neuro-fuzzy control system were tested. Dynamic characteristics of neuro-fuzzy control system made cold storage facility conducted precooling ability and minimized hunting.

• 한국지능시스템학회논문지
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• 제13권2호
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• pp.137-147
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• 2003

새로운 3차원 도립진자 시스템의 구현 및 퍼지제어에 관하여 논한다. 기존의 1차원 또는 2차원 도립진자 시스템과 달리, 3차원 도립진자 시스템은 상하 운동을 포함하는 인간의 도립진자 제어행위를 적절히 모사할 수 있는 새로운 시스템이다. 3차원 도립진자 시스템의 특성 분석과 퍼지제어기 설계를 위하여 3축 직교로봇과 도립진자를 포함하는 기구부의 동력학식을 유도한다. 로봇의 여유자유도와 제한된 작업영역을 고려하면서 도립진자의 요오(yaw) 및 피치(pitch)각을 제어하기 위한 퍼지제어기 설계 방법을 제안한다. 개발된 PC 기반의 다축제어보드를 이용한 실험 결과를 통하여 제안된 시스템의 성능을 검증한다.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2003년도 추계학술대회 논문집
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• pp.69-72
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• 2003

The paper presents implementation of a PV fuzzy logic power tracking controller. Using maximum power point tracker with the intermediate converter can increase the system efficiency by matching the PV system to the load. A new fuzzy MPPT is proposed, where fuzzy inputs parameters are dp/dI and the last incremental of duty of duty ratio $L{\delta}D$, and the output is the new incremental value $(new{\delta}D)$ according to the maximum power point under various illumination levels.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권12호
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• pp.562-570
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• 2002

The Purpose of this paper is to design and manufacture an educational system in order to demonstrate the causes and effects of electromagnetic induction.'rho educational system described in this study is a "jumping ring apparatus". This system demonstrates the principle of electromagnetic induction, a force from AC sources, Lenz's law of repulsion and transformer. The educational system is composed of a jumping ring apparatus, a sensor array, encoder, A/D converter, D/A converter and nonlinear controller. The educational system is controlled by 586 PC using Turbo C program. The sensor array is composed of 20 optical sensors. The nonlinear controller consists of nonlinear control algorithm and control board included SCR, FET and phase controller. The A/D converter is used to show the height of ring position to analog for an education purpose. The control signal calculated from the nonlinear control of algorithm send control board through 8 bit D/A convertor. Experiment results are given to verify that Proposed nonlinear controller is useful in on line control of the educational system.al system.