• 전력전자학회논문지
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• 제2권1호
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• pp.39-45
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• 1997

초고속 전동기 구동 시스템을 위하여 간접 신경회로망 제어기를 제안하였다. 고속의 가변 전동기구동에서의 속도응답은 긴 정착시간과 높은 오버슈트의 영향에 있게 되므로 고성능을 위하여 신경회로망 제어기와 신경회로망 에뮬레이터로 구성된 제어기를 사용하였으며, 신경회로망 에뮬레이터는 고속 전동기의 정수와 특성을 동정하는데 사용하였고, 제어기의 학습은 접속강도가 백프로퍼게이션에 의해 조절되도록 하였다. 그리고 시뮬레이션과 실험을 통하여 제안된 시스템의 특성과 장점을 확인하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.1954-1955
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• 2007

This paper presents a modeling method of speed controller for DC motor drive system by using the Embedded Target for TI C2000 DSP in MATLAB/SIMULINK. Speed controller is easily designed and implemented by using the MATLAB/SIMULINK program, and speed control response and stability of the DC motor can be improved. Feedback of motor speed is processed through C28x QEP(Quadrature Encoder Pulse) from encoder pulse. The controller is designed as PI speed controller. Simulation program is drawn using SIMULINK. Then a real-time program for speed control of the DC motor is downloaded into the eZdsp F2811 control board. Speed control response is verified through simulations and experiments.

• 전기학회논문지P
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• 제60권3호
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• pp.144-149
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• 2011

Currently, switched reluctance motors are studied to increase energy efficient. The switched reluctance motor is possible to control higher speed and torque than the induction motors and composition is simple. Current and rotor speed are considered to install the switched reluctance motor. In this paper, Simulink model of the switched reluctance motor is suggested and the adaptive controller is applied to the model for the speed control. Asymmetry converter including the IGBT is concerned with real-time control and the speed of switched reluctance motor is controlled by installing adaptive controller.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권10호
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• pp.443-445
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• 2006

Generally PI controller is used to control the speed of an induction motor. It has the good performance of speed control in case of adjusting the control parameters. But it occurred the problem to change the control parameters in the change of operation condition. In order to solve this problem, Fuzzy control or Artificial neural network is introduced in the speed control of an induction motor. However, Fuzzy control have the problems as the difficulties to change the membership function and fuzzy rule and the remaining error Also Neural network has the problem as the difficulties to analyze the behavior of inner part. Therefore, the study on the combination of two controller is proceeded. In this paper, Fuzzy-neural controller to make up these controllers in parallel is proposed and the speed control of an induction motor is performed using the proposed controller Through the experiment, the fast response and good stability of the proposed speed controller is proved.

• 전기학회논문지P
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• 제55권3호
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• pp.117-122
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• 2006

The paper is proposed artificial neural network(ANN) sensorless control of induction motor drive with fuzzy learning control-fuzzy neural network(FLC-FNN) controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also this paper is proposed. speed control of induction motor using FLC-FNN and estimation of speed using ANN controller. The back Propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed so that the actual state variable will coincide with the desired one. The proposed control algorithm is applied to induction motor drive system controlled FLC-FNN and ANN controller, Also, this paper is proposed the analysis results to verify the effectiveness of the FLC-FNN and ANN controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.216-218
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• 2006

The paper is proposed high performance control of induction motor drive with adaptive fuzzy logic controller(AFLC). Also, this paper is proposed speed control of induction motor using AFLC and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The proposed control algorithm is applied to induction motor drive system controlled AFLC and ANN controller. And this paper is proposed the results to verify the effectiveness of the AFLC and ANN controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.282-284
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• 2006

Generally PI controller is used to control the speed of an induction motor. It has the good performance of speed control in case of adjusting the control parameters. But it occurred the problem to change the control parameters in the change of operation condition. In order to solve this problem, Fuzzy control or Artificial neural network is introduced in the speed control of an induction motor. However, Fuzzy control have the problems as the difficulties to change the membership function and fuzzy rule and the remaining error. Also Neural network has the problem as the difficulties to analyze the behavior of inner part. Therefore, the study on the combination of two controller is proceeded. In this paper, Speed controller of an induction motor based fuzzy-neural network is proposed and the speed control of an induction motor is performed using the proposed controller. Through the experiment, the fast response and good stability of the proposed speed controller is proved.

• 한국컴퓨터정보학회논문지
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• 제8권3호
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• pp.163-169
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• 2003

본 연구에서 개발하고자 하는 기기의 모델은 마이크로 컨트롤을 이용하여 모터속도를 제어 할 수 있는 출력 전자 변속기로써, 자동제어 방식을 사용하여 Switching frequency를 1,000MHz까지 가능한 형태로 변환하는 자동 변속장치이다. 연속출력전류는 5A, 11A, 18A, 25A, 35A, 50A이며, 사용되는 전압은 9V에서 최대 18V까지 가능하도록 설계하였다. Micro-controller 의 software와 hardware의 블록 다아이그램을 고안하였으며, 전력손실을 막기 위해 자동적으로 3.7V에서 Auto Cut-Off기능이 있도록 구성하였다.

• Journal of Electrical Engineering and information Science
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• 제2권6호
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• pp.82-89
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• 1997

In this paper, an adaptive speed controller with an FNN(Feedforward Neural Network) is proposed for servo motor drives. Generally, the motor system has nonlinearities in friction, load disturbance and magnetic saturation. It is necessary to treat the nonlinearities for improving performance in servo control. The FNN can be applied to control and identify a nonlinear dynamical system by learning capability. In this study, at first, a robust speed controller is developed by Lyapunov stability theory. However, the control input has discontinuity which generates an inherent chattering. To solve the problem and to improve the performances, the FNN is introduced to convert the discontinuous input to continuous one in error boundary. The FNN is applied to identify the inverse dynamics of the motor and to control the motor using coordination of feedforward control combined with inverse motor dynamics identification. The proposed controller is developed for an SR motor which has highly nonlinear characteristics and it is compared with an MRAC(Model Reference Adaptive Controller). Experiments on an SR motor illustrate te validity of the proposed controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 B
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• pp.1027-1028
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• 2006

In this paper, design of current and speed controller for DC motor drive system using Embedded Target for TI C2000DSP in Matlab/Simulink is introduced. Current and speed controller is designed and implemented using program simply and easily, and speed control response of DC motor can be advanced. Current and speed control of DC motor is carried in eZdsp F2812 control board using Embeded Target for TI C2000DSP in Matlab/Simulink. Speed feedback is processed through A/D converter using tacho generator as speed sensor, and current feedback is processed through A/D converter using hall sensor as current sensor. Controller is designed to PI current controller and PI speed controller. Current and speed response is verified through simulations and experiments.