Journal of the Institute of Electronics Engineers of Korea SC (전자공학회논문지SC)

The Institute of Electronics and Information Engineers (대한전자공학회)
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STPI Controller of IPMSM Drive using Neural Network

신경회로망을 이용한 IPMSM 드라이브의 STPI 제어기

  • Published : 2007.03.25
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Abstract

This paper presents self tuning PI(STPI) controller of IPMSM drive using neural network. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed gain PI controller, STPI controller proposes a new method based neural network. STPI controller is developed to minimize overshoot, rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network 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 results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

본 논문은 신경회로망을 이용한 IPMSM 드라이브의 자기동조 PI 제어기를 제시한다. 일반적으로 수치제어장치 처리는 고정된 이득값을 가진 PI 제어기를 이용한다. 고정된 이득값을 가진 PI 제어기는 어떠한 환경에서는 양호하게 동작할 수 도 있다. 고정된 이득값을 가진 PI 제어기의 강인성을 증가시키기 위하여 신경회로망을 기반으로한 새로운 방법인 STPI 제어기를 제시하였다. STPI 제어기는 속도, 부하토크, 관성과 같은 파라비터가 갑자기 변화하였을 때 오버슈트, 상승시간, 안정화시간을 최소화한다. 또한 본 논문에서는 신경회로망을 이용하여 속도를 제어하고 ANN 제어기를 이용하여 속도를 추정한다. 신경회로망의 역전파 알고리즘 기법은 전동기 속도의 실시간 추정을 제시한다. IPMSM의 속도제어의 결과는 이득값 동조의 효용성을 보여준다. 그리고 STPI 제어기는 고정된 이득값을 가진 PI 제어기에 비하여 강인성 광범위한 운전영역 부하 왜란등에 대하여 우수한 성능을 나타낸다.

Keywords

References

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