대한전기학회:학술대회논문집 (Proceedings of the KIEE Conference)

대한전기학회 (The Korean Institute of Electrical Engineers)
권호 표지

AIPI 제어기를 이용한 IPMSM의 고성능 제어

High Performance Control of IPMSM using AIPI Controller

  • 김도연 (순천대학교 공과대학 정보통신공학부) ;
  • 고재섭 (순천대학교 공과대학 정보통신공학부) ;
  • 최정식 (순천대학교 공과대학 정보통신공학부) ;
  • 정철호 (순천대학교 공과대학 정보통신공학부) ;
  • 정병진 (순천대학교 공과대학 정보통신공학부) ;
  • 정동화 (순천대학교 공과대학 정보통신공학부)
  • Kim, Do-Yeon (School of Information & Communication Engineering, Sunchon National Univ.) ;
  • Ko, Jae-Sub (School of Information & Communication Engineering, Sunchon National Univ.) ;
  • Choi, Jung-Sik (School of Information & Communication Engineering, Sunchon National Univ.) ;
  • Jung, Chul-Ho (School of Information & Communication Engineering, Sunchon National Univ.) ;
  • Jung, Byung-Jin (School of Information & Communication Engineering, Sunchon National Univ.) ;
  • Chung, Dong-Hwa (School of Information & Communication Engineering, Sunchon National Univ.)
  • 발행 : 2009.04.23
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초록

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 artificial intelligent-PI(AIPI) 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.

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