조명전기설비학회논문지 (Journal of the Korean Institute of Illuminating and Electrical Installation Engineers)

  • 제21권5호
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  • Pages.32-43
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  • 2007
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  • 1229-4691(pISSN)
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  • 2287-5034(eISSN)
한국조명전기설비학회 (The Korean Institute of IIIuminating and Electrical Installation Engineers)
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적응학습 퍼지-신경회로망에 의한 IPMSM의 최대토크 제어

Maximum Torque Control of IPMSM with Adoptive Leaning Fuzzy-Neural Network

  • 정동화 (순천대학교 정보통신공학부) ;
  • 고재섭 (순천대학교 대학원 전기공학과) ;
  • 최정식 (순천대학교 대학원 전기공학과)
  • 발행 : 2007.06.30
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초록

IPMSM은 하중에 비하여 고출력으로 인하여 전기자동차에 널리 보급되고 있다. 본 논문은 적응 학습 퍼지-신경회로망과 ANN을 이용한 IPMSM드라이브의 최대토크 제어를 제시한다. 이러한 제어 방법은 인버터의 정격전류 및 전압값의 범위를 고려한 전속도 영역에 적용 된다. 본 논문은 적응학습 퍼지-신경회로망을 이용하여 IPMSM의 속도제어와 ANN을 이용하여 속도를 추정을 제시한다. 신경회로망의 역전파 알고리즘은 전동기 속도의 실시간 추정을 제시하는데 사용된다. 제시된 제어 알고리즘은 적응학습 퍼지-신경회로망과 ANN 제어기를 IPMSM 드라이브에 적용된다. 최대토크에 의해 제어된 동작 특성은 세부적으로 실험한다. 또한 본 논문은 적응 학습 퍼지 신경회로망과 ANN의 효과를 결과 분석을 통해 제시한다.

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 and voltage rated value. This paper proposes speed control of IPMSM using adaptive learning 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 learning 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 learning fuzzy neural network and artificial neural network.

키워드

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