• 제어로봇시스템학회논문지
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• 제19권12호
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• pp.1125-1131
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• 2013

Nowadays, embedded software development using the MATLAB/Simulink system is gradually emerging. Studies generating the parts of embedded S/W in a Rapid Prototype are presented. In this paper, a method to generate the entire embedded S/W of enhanced AC motor control is proposed. High performance motor control could not be achieved with the basic Simulink library and RAppID Toolbox library as it does not have PWM based Interrupt, an ASAC (Analog Sensing for AC Motors) function and other special functions of the Freescale MPC555x. Consequently, the required libraries for enhanced AC motor control are created by Legacy code tool, TLC (Target Language Compiler) and S-Function (System-Function) of MATLAB/ Simulink and utilized in the Rapid Prototype. Motor control performance and execution time are compared automatically to the generated-code S/W with the hand coded S/W. The IPMSM (Interior Permanent Magnet Synchronous Motor) and MPC5553 board that were designed as the AC motor controller for hybrid electrical vehicle are used for the test. The performances meet the requirements and satisfactory results are acquired.

• Advances in materials Research
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• 제4권4호
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• pp.227-233
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• 2015

We present a method, supported by theoretical analysis, for optimizing the usage of the critical rare earth element dysprosium in $Nd_2Fe_{14}B$ (NdFeB)-based permanent magnets. In this method, we use Dy selectively in locations such as magnet edges and faces, where demagnetization factors are largest, rather than uniformly throughout the bulk sample. A200 nm thick Dy film was sputtered onto a commercial N-38, NdFeB magnets with a thickness of 3 mm and post-annealed at temperatures from $600-700^{\circ}C$. Magnets displayed enhanced coercivities after post-annealing and as much as a 5 % increase in the energy product, while requiring a total Dy content of 0.06 wt. % - a small fraction of that used in the commercial grade Dy-NdFeB magnets. By assuming all Dy diffused into NdFeB magnets, the improvement in energy product corresponds to a saving of over 1% Dy (critical element). Magnets manufactured using this technique will therefore be higher performing which would potentially broaden the application space of these magnets in the traction motors of hybrid and pure electric vehicles, and wind generators.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 하계학술대회 논문집 B
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• pp.1251-1253
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• 2004

This paper is proposed a fuzzy neural network controller based on the vector controlled surface permanent magnet synchronous motor(SPMSM) drive system. 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 SPMSM using neuro-fuzzy control(NFC) 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 back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

• 한국분말재료학회지
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• 제17권1호
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• pp.23-28
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• 2010

HDDR treated anisotropic Nd-Fe-B powders have been widely used for the sheet motors and the sunroof motors of hybrid or electric vehicles, due to their excellent magnetic properties. Microstructural alignment of HDDR treated powders are mostly depending on the hydrogen reaction in disproportionation step, so the specific method to control hydrogenation reaction is required for improving magnetic properties. In disproportionation step, hydrogenation pressure and reaction time were controlled in the range of 0.15~1.0 atm for 15~180 min in order to control the micorstructural alignment of $Nd_2Fe_{14}B$ phase and, at the same time, to improve remanence of HDDR treated magnet powders. In this study, we could obtain a well aligned anisotropic Nd-Fe-B-Ga-Nb alloy powder having high remanence of 12 kG by reducing hydrogen pressure down to 0.3 atm in disproportionation step.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2006년도 춘계학술대회 논문집
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• pp.309-314
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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. This paper proposes maximum torque control of IPMSM drive using adaptive learning fuzzy neural network and artificial neural network. This control method is applicable over the entire speed range which considered the limits of the inverter's current md 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 adaptive teaming fuzzy neural network and artificial neural network. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper proposes speed control of IPMSM using adaptive teaming fuzzy neural network and estimation of speed using artificial neural network. 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 adaptive teaming fuzzy neural network and artificial neural network, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the adaptive teaming fuzzy neural network and artificial neural network.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2009년도 춘계학술대회 논문집
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• pp.420-423
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• 2009

The conventional fixed gain PI controller is very sensitive to step change of command speed, parameter variation and load disturbances. The precise speed control of interior permanent magnet synchronous motor(IPMSM) drive becomes a complex issue due to nonlinear coupling among its winding currents and the rotor speed as well as the nonlinear electromagnetic developed torque. Therefore, there exists a need to tune the PI controller parameters on-line to ensure optimum drive performance over a wide range of operating conditions. This paper is proposed hybrid intelligent-PI(HIPI) controller of IPMSM drive using adaptive learning mechanism(ALM) and fuzzy neural network(FNN). The proposed controller is developed to ensure accurate speed control of IPMSM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. The PI controller parameters are optimized by ALM-FNN at all possible operating condition in a closed loop vector control scheme. The validity of the proposed controller is verified by results at different dynamic operating conditions.

• Progress in Superconductivity
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• 제6권1호
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• pp.79-83
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• 2004

We fabricated Bi-2223/Ag superconductor tape with 55 filaments and estimated the magnetic field dependence of critical current (I$_{c}$) and index n (n) up to 30 T at 4.2 K. The I$_{c}$ and n were characterized as a function of external magnetic field parallel to the tape surface on increasing and decreasing field, using a 35 T hybrid magnet. The $I_{c}$ was estimated to be 325 A, and n was 32, 22, and 26 in the electric field range of $0.1 ∼1\mu$V/cm, 1∼10 $\mu$V/cm, and 0.1∼10 $\mu$V/cm, respectively, under self-field at 4.2 K. It was observed that $I_{c}$ was dependent on magnitude of magnetic field and it decreased exponentially as the field increased; in a parallel and increasing field, It was 128 A at 30 T which is approximately 40% of critical current in self-field. In addition, the $I_{c}$ was higher on decreasing field than that on increasing one. On the other hand, n did not significantly depend on field strength up to 30 T, nor varied on whether increasing or decreasing field; n value in 0.1∼1 $\mu$V/cm was 23.0$\pm$5.2 and 27.8$\pm$8.0 on increasing and decreasing field, respectively. The n value on decreasing magnetic field was slightly higher than that on increasing field. This hysteretic behavior of n was similar to that of$ I_{c}$, which is related to the trapped flux at the grain boundary.ary.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2007년도 추계학술대회 논문집
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• pp.313-318
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• 2007

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive fuzzy neural network controller and artificial neural network(ANN). This control method is applicable over the entire speed range which 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 Adaptive-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 reposes speed control of IPMSM using Adaptive-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 a lied to IPMSM drive system controlled Adaptive-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the Adaptive-FNN and ANN controller.

• 조명전기설비학회논문지
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• 제23권8호
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• pp.57-66
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• 2009

종래의 고정된 이득을 가진 PI 제어기는 지령속도, 부하변화 등과 같은 파라미터 변동에 대해서 매우 민감하다. IPMSM 드라이브의 정확한 속도제어는 비선형적인 전자기적 발생저항뿐만 아니라 회전자 속도와 권선저항사이의 비선형적 관계 때문에 복잡한 문제점이 있다. 따라서 광범위한 동작상태에서 최적 제어를 위해 PI 제어기의 이득값을 실시간으로 조절해야한다. 본 논문은 FNN과 ALM을 이용하여 IPMSM 드라이브의 HIPI 제어기를 제시한다. 제시된 제어기는 ANN을 이용하여 속도를 추정하고, 시스템 외란에 대해서 IPMSM 드라이브의 고성능 속도제어를 제시한다. PI 제어기의 이득값은 모든 동작상태에서 ALM-FNN에 의해 최적화 되어진다. 제시된 제어기는 다양한 동작상태에 대한 분석을 통해 타당성을 입증한다.

• 대한기계학회논문집B
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• 제35권2호
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• pp.189-196
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• 2011

최근, 승용차는 물론 대형차에서도 하이브리드시스템 등의 전기동력방식을 도입하려는 연구개발이 활발하다. 기존의 내연기관을 대체하는 전기구동모터를 이용하여 차량을 구동하는 전기동력방식을 채용하는 것이 한층 더 효율적이며 가속성능, 승차감 등의 면에서도 유리한 점이 많다. 그러나 모터는 엔진과 전혀 다른 특징을 갖기 때문에 동력성능과 에너지효율에 관한 적합한 평가수법을 검토할 필요가 있다. 본 논문에서는 전기구동버스에 대한 구동모터 시스템을 다이나모미터 상에서 운전하여, 노선버스의 운행패턴을 반영한 모의운전을 실시, 가속성능 및 에너지변환효율, 회생효과의 평가방법 등을 고찰하였다. 그 결과 실 주행 패턴운전에 대한 모터일률, 전력량 등의 계측으로부터 모터 변환효율은 90% 전후, 회생전력량은 요구전력량의 40% 이상으로 평가되었다.