• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 학술대회 논문집 정보 및 제어부문
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• pp.566-569
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• 2005

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using artificial intelligent(AI) controller. The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AI controller. This paper is proposed speed control of IPMSM using learning mechanism fuzzy neural network(LM-FNN) 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 proposed control algorithm is applied to IPMSM drive system controlled LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also. this paper is proposed the experimental results to verify the effectiveness of AI controller.

• 대한기계학회논문집B
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• 제29권2호
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• pp.224-231
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• 2005

The present study investigates flow characteristics in an optical disc drive (ODD). Detailed knowledge of the flow characteristics is essential to analyze flow-induced noise and vibration, forced convection and flow friction loss. The ODD used in a personal computer is used for the experiment and rotating velocity of disc is under the 4500 rpm. Time-resolved velocity component and velocity spectrum are obtained using the laser Doppler anemometry (LDA), and the flow patterns induced by rotating disc in the ODD are calculated by a commercial finite volume method at the same time. The results show that the front holes reduce flow-induced noise and the position of pickup body only affects flow near the window. Furthermore, it is possible for cooling of heat sources in the drive through measuring the flow fields under the tray. In addition, the numerical results are well matched up to the experimental results, therefore, the validation of the numerical results can be achieved. From the validation of numerical results, it is possible to predict the flow characteristics of the region where it is unable to conduct the experiment.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2001년도 춘계학술대회논문집
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• pp.911-916
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• 2001

Structural acoustic modification method based on the structural mobility analysis is applied to reduce the structure-borne noise radiated from hard disk drive system. Sound intensity techniques and ODS(Operational Deflection Shape) techniques are also used in order to provide the structural acoustic information for the mobility modification. The sound intensity is for the acoustic visualization of the noise source locations, and the ODS is for the visualization of the vibration pattern and its dynamic characteristics of the noise sources. Using visualization information of sound and vibration, local structural input mobility is reduced in the frequency band of interest by designing asymmetrical wave-stringer structure in the wave-number domain as well as frequency domain. The overall sound pressure level is reduced by 4dB and its controlled sound power radiated from the disk drive is proved to under 2.8Bel in idle-spinning mode and 3.1 Bel in random-seeking mode, which are the lowest noise levels in the hard disk drive industry.

• 한국정보통신학회논문지
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• 제6권6호
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• pp.879-884
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• 2002

산업 현장에서 필수적인 설비인 공기 압축기는 스크루, 왕복동 및 터보 압축기가 사용되고 있다. 근래에 터보 압축기가 넓이 사용되고 있는데, 터보 압축기는 구조적으로 임펠러를 고속회전 시켜야한다. 범용전동기에 높은 기어비를 가진 기어박스를 이용하여 구현하였는데, 이는 관성 모멘트, 마찰손 및 압축기의 크기를 증가시켰다. 최근에 터보 압축기의 연구는 초고속 동기전동기를 장착하여 기어박스를 제거하여 크기와 마찰손을 최소화하는 방향으로 진행되고 있다. 본 연구에서는 150마력 70,000rpm 직접 구동방식의 터보 압축기를 개발하기 위하여 초고속 동기전동기 센서리스 벡터 구동시스템을 개발하였고, 이를 적용하여 직접 구동방식 터보 압축기를 개발하였다.

• Journal of Power Electronics
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• 제12권3호
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• pp.468-476
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• 2012

The interior permanent magnet synchronous motor (IPMSM) has been widely used in electric vehicle applications due to its excellent power to weigh ratio. This paper proposes the maximum torque control of an IPMSM drive using an adaptive learning (AL) fuzzy neural network (FNN) and an artificial neural network (ANN). This control method is applicable over the entire speed range while taking into consideration the limits of the inverter's rated current and voltage. This maximum torque control is an executed control through an optimal d-axis current that is calculated according to the operating conditions. This paper proposes a novel technique for the high performance speed control of an IPMSM using AL-FNN and ANN. The AL-FNN is a control algorithm that is a combination of adaptive control and a FNN. This control algorithm has a powerful numerical processing capability and a high adaptability. In addition, this paper proposes the speed control of an IPMSM using an AL-FNN, the estimation of speed using an ANN and a maximum torque control using the optimal d-axis current according to the operating conditions. The proposed control algorithm is applied to an IPMSM drive system. This paper demonstrates the validity of the proposed algorithms through result analysis based on experiments under various operating conditions.

• 대한전기학회논문지:전기기기및에너지변환시스템부문B
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• 제55권2호
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• pp.89-97
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. The paper is proposed maximum torque control of IPMSM drive using learning mechanism-fuzzy neural network(LM-FNN) controller and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_{d}$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using LM-FNN controller and ANN 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 IPMSM using LM-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 proposed control algorithm is applied to IPMSM drive system controlled LM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the analysis results to verify the effectiveness of the LM-FNN and ANN controller.

• 조명전기설비학회논문지
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• 제20권10호
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• pp.27-34
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• 2006

본 논문은 드라이브의 간적벡터제어에서 ANN을 이용하여 유도전동기의 회전자 저항을 온라인 추정하기 위한 새로운 기법을 제시한다. 약전파 알고리즘은 신경회로망의 학습을 위해 사용된다. 신경회로망의 실제 상태값과 유도전동기의 요구값 사이의 오차는 신경회로망 모델의 하중값 조절을 위하여 역전파 하여 실제값이 요구값을 추정하도록 한다. 드라이브의 회전자 저항, 토크, 자속응답 성능등 이러한 추정기의 성능은 고유값으로부터 회전자 저항을 연구하게 된다. 회전자 저항은 유도전동기 드라이브의 벡터제어에서 제시된 ANN을 사용하여 추정한다.

• 대한전기학회논문지
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• 제39권11호
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• pp.1174-1182
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• 1990

A hgih efficiency and good dynamic performance drive system of an induction motor is presented in this paper using vector control technique. If the induction motor is driven under light loads with rated flux, the iron loss is excessively large compared with the copper loss, resulting in poor motor efficiency. High efficiency drive of an induction motor can be achieved by adjusting the flux level which leads the total motor loss to be a minimum value. Generally reducing the flux degrades the dynamic performance, but the dynamic performance of the proposed system is also maintained high. If the d-axis is coincident with rotor flux phasor in synchronous rotating reference frame, the stator current can be decoupled as flux component and torque component. At steady state, the developed motor torque is proportional to the product of the flux and torque component. The combination of the two components minimizing the motor loss could be found with numerical method. As the procedure to obtain the optimal combination is too hard, it is found experimentally. The system block diagram is suggested for maximum efficiency control. The proposed system is studied through digital simulation and verified with experiment. The experimental results show the possiblity of a high efficiency drive with good dynamic performance of maximum efficiency control.

• 조명전기설비학회논문지
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• 제24권5호
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• pp.16-28
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• 2010

본 논문은 AIPI 및 ANN에 의한 SynRM 드라이브의 최대토크 제어를 제시한다. 본 논문은 인버터의 정격 전압과 전류의 한계 조건을 고려하여 전 속도영역에서 최대토크제어를 제시한다. 속도에 따라 각 제어모드에서 최대토크를 발생하기 위한 최적의 전류값을 계산하고. 계산된 최적전류를 이용하여 최대토크 제어를 수행한다. 제시된 최대토크 제어 알고리즘은 AIPI와 ANN 제어기와 함께 SynRM 드라이브에 적용하여 동작특성을 분석하고 그 타당성을 제시한다.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권3호
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• pp.110-114
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• 2006

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. In this paper maximum torque control of IPMSM drive using artificial intelligent(AI) controller is proposed. The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using AI controller. This paper is proposed speed control of IPMSM using adaptive learning mechanism fuzzy neural network(ALM-FNN) 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 proposed control algorithm is applied to IPMSM drive system controlled ALM-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper is proposed the experimental results to verify the effectiveness of AI controller.