• 전력전자학회논문지
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• 제18권1호
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• pp.18-25
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• 2013

In this paper, a nonlinear controller based on adaptive back-stepping method is proposed for high performance operation of Interior Permanent Magnet Synchronous Motor (IPMSM). First, in order to improve the performance of speed tracking, a nonlinear back-stepping controller is designed. In addition, since it is difficult to achieve the high quality control performance without considering parameter variation, a parameter estimator is included to adapt to the variation of load torque in real time. Finally, for the efficiency of power consumption of the motor, controller is designed to operate motor with the minimum current for the required maximum torque. The proposed controller is tested through experiment with a 1-hp Interior Permanent Magnet Synchronous Motor (IPMSM) for the angular velocity reference tracking performance and load torque volatility estimation, and to test the Maximum Torque per Ampere (MTPA) operation. The result verifies the efficacy of the proposed controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 심포지엄 논문집 정보 및 제어부문
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• pp.219-220
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• 2008

The paper is proposed maximum torque control of SynRM drive using adaptive fuzzy neuro inference system(AFNIS) 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. The proposed control algorithm is applied to SynRM drive system controlled AFNIS 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 AFNIS and ANN controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.155-157
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• 2006

The paper is proposed maximum torque control of SynRM drive using adaptive learning mechanism-fuzzy neural network(ALM-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. The proposed control algorithm is applied to SynRM 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 analysis results to verify the effectiveness of the ALM-FNN and ANN controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 B
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• pp.729-730
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• 2006

The paper is proposed maximum torque control of SynRM drive using adaptive fuzzy neural network(A-FNN) controller and artificial neural network(ANN). For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled A-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 A-FNN and ANN controller.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 B
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• pp.731-732
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• 2006

The paper is proposed maximum torque control of IPMSM drive using adaptive learning mechanism-fuzzy neural network (ALM-FNN) and artificial neural network(ANN). For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. The proposed control algorithm is applied to IPMSM drive system controlled ALM-FNN and ANN, 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 ALM-FNN and ANN.

• 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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 춘계학술대회 논문집 에너지변화시스템부문
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• pp.26-28
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• 2009

This paper proposes maximum torque control of IPMSM drive using adaptive teaming mechanism-fuzzy neural network (ALM-FNN) controller, model reference adaptive fuzzy tonal(MFC) 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 ALM-FNN, MFC and ANN controller. The proposed control algorithm is applied to IPMSM drive system controlled ALM-FNN, MFC 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 ALM-FNN, MFC and ANN controller.

• 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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 추계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.198-201
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• 2005

The paper is proposed maximum torque control of IPMSM drive using adaptive learning mechanism-fuzzy neural network (ALM-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. 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 analysis results to verily the effectiveness of the ALM-FNN and ANN controller.

• 한국정보전자통신기술학회논문지
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• 제12권4호
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• pp.359-365
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• 2019

본 연구는 트랙터 변속기의 성능평가, 내구성 향상 및 최적 설계 등을 위한 기초 연구로써 로타리 작업에 따른 78 kW급 농업용 트랙터의 엔진 토크를 CAN 통신을 이용하여 수집하고, 등가 토크를 분석하였다. 신뢰성 높은 트랙터 개발을 위해서는 실제 농작업 환경에서 발생하는 부하를 등가 토크로 변환하여, 분석하는 것이 필요하다. 등가 토크는 대표적인 누적 손상법인 Palmgren-Miner 식으로 작업 및 선회구간에 대하여 계산되었으며, 각각 229.2, 136.7 Nm로 나타났다. 로타리 작업구간에서 최대 및 평균 토크는 각각 336.0, 234.4 Nm로 나타났으며, 선회구간의 최대 및 평균 토크는 288.0, 134.6 Nm로 나타났다. 로타리 작업구간에서의 엔진 토크는 PTO를 통해 토양을 경운하기 때문에 선회구간보다 높게 나타났다. 엔진의 최대 및 정격 토크는 각각 387.0, 323.0 Nm로, 로타리 작업 및 선회구간의 등가 토크보다 각각 183%, 136% 높게 나타났다. 국내 트랙터 회사는 일반적으로 엔진의 정격 토크를 기준으로 변속기를 설계하기 때문에, 실제 작업 토크와 다소 차이가 있다. 그러므로 최적 설계를 위해 실제 작업 토크를 고려하는 것이 필요할 것으로 판단된다.