• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 추계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.68-70
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• 2008

This paper proposes FNN based PI controller for high performance control of SynRM Drive. Traditional PI controller can't be obtained good performance because it has fixed gain. Therefore, in this paper, FNN based PI controller that gain of PI controller is tuned use FNN proposes. FNN based PI controller proposed in this paper can be obtained excellent performance more than traditional PI controller. Algorithm proposed in this paper make a analysis and prove valid.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2009년도 춘계학술대회 논문집
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• pp.408-411
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• 2009

In this paper, conventional PI, fuzzy neural network(FNN) and adaptive teaming mechanism(ALM)-FNN for rotor field oriented controlled(RFOC) induction motor are studied comparatively. The widely used control theory based design of PI family controllers fails to perform satisfactorily under parameter variation nonlinear or load disturbance. In high performance applications, it is useful to automatically extract the complex relation that represent the drive behaviour. The use of learning through example algorithms can be a powerful tool for automatic modelling variable speed drives. They can automatically extract a functional relationship representative of the drive behavior. These methods present some advantages over the classical ones since they do not rely on the precise knowledge of mathematical models and parameters. Comparative study of PI, FNN and ALM-FNN are carried out from various aspects which is dynamic performance, steady-state accuracy, parameter robustness and complementation etc. To have a clear view of the three techniques, a RFOC system based on a three level neutral point clamped inverter-fed induction motor drive is established in this paper. Each of the three control technique: PI, FNN and ALM-FNN, are used in the outer loops for rotor speed. The merit and drawbacks of each method are summarized in the conclusion part, which may a guideline for industry application.

• 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.

• 조명전기설비학회논문지
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• 제24권4호
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• pp.33-42
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• 2010

벡터제어를 적용한 유도전동기 드라이브는 고성능 제어를 위하여 산업 적용분야에 광범위하게 사용되고 있다. 그러나 유도전동기의 모델은 비선형이고 복잡하기 때문에 포화, 온도변화, 외란 및 파라미터 변동등에 의해 성능 및 신뢰성이 저하된다. 이러한 가변속 드라이브를 제어하기 위하여 종래의 PI와 같은 제어기들이 일반적으로 사용되어졌다. 이러한 제어기들은 이상적인 벡터제어 상태에서도 광범위한 동작영역에서 양호한 성능을 나타내는데 한계를 가지고 있다. 본 논문은 퍼지제어, 신경회로망, 적응 퍼지제어로 구성된 FNN(Fuzzy-Neural Network)-PI 제어기 기반 자기동조 PI 제어기와 ANN을 이용한 속도추정을 제시한다. FNN-PI, AFC, ANN 제어기를 이용한 제어 알고리즘은 유도전동기 드라이브 시스템에 적용하여 그 결과를 분석하고 제어기의 효용성을 입증한다.

• 조명전기설비학회논문지
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• 제26권12호
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• pp.9-20
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• 2012

This paper proposes the efficiency optimization control of interior permanent magnet synchronous motor(IPMSM) drive using series connected-fuzzy neural network PI(SC-FNPI) controller. The PI controller is generally used to control IPMSM drive in industrial field. However, the PI controller has problem which is falling control performance about parameter variation such as command speed, load torque and inertia due to fixed gain of PI controller. Therefore, to improve performance of PI controller, this paper proposes SC-FNPI controller adjusted input of PI controller by FNN controller according to operating conditions. Also, this paper proposes efficiency optimization control which is improving efficiency with minimize loss. The SC-FNPI controller proposed in this paper is compared control performance with conventional FNN and PI controller about command speed, load torque and inertia variation. And the efficiency optimization control is compared with $i_d=0$ control about loss and efficiency. The SC-FNPI controller proposed in this paper shows more excellent control performance for rising time, overshoot and steady-state error. Also efficiency optimization control is increased efficiency by reducing loss.

• 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.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2008년도 춘계학술대회 논문집
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• pp.395-398
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• 2008

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. In order to maximize the efficiency in such applications, this paper proposes the FNN(Fuzzy Neural-Network)-Pl controller. The controllable electrical loss which consists of the copper loss and the iron loss can be minimized by the error back propagation algorithm(EBPA). This paper considers the parameter variation about the motor operation. The operating characteristics controlled by efficiency optimization control are examined in detail.

• 조명전기설비학회논문지
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• 제25권9호
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• pp.33-43
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• 2011

Since the fuzzy neural network(FNN) is universal approximators, the development of FNN control systems have also grown rapidly to deal with non-linearities and uncertainties. However, the major drawback of the existing FNNs is that their processor is limited to static problems due to their feedforward network structure. This paper proposes the recurrent FNN(RFNN) for high performance and robust control of SynRM. RFNN is applied to speed controller for SynRM drive and model reference adaptive fuzzy controller(MFC) that combine adaptive fuzzy learning controller(AFLC) and fuzzy logic control(FLC), is applied to current controller. Also, this paper proposes speed estimation algorithm using artificial neural network(ANN). The proposed method is analyzed and compared to conventional PI and FNN controller in various operating condition such as parameter variation, steady and transient states etc.

• 조명전기설비학회논문지
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• 제23권8호
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• pp.33-40
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• 2009

본 논문에서는 HAI(Hybrid Artificial Intelligent) 제어기반의 SV PWM 방식을 이용한 IPMSM의 고성능 제어를 제시한다. HAI 제어기는 적응 퍼지제어 및 신경회로망의 장점을 혼합 적용한다. SV PWM 방식은 지금까지 산업용 전동기 제어분야에 적용되고 있고 출력전류의 고조파 비율, 스위칭 주파수 및 응답특성을 향상시키는 수 있는 기법이다. HAI 제어기는 지령전압을 계산할 때 발생되는 문제점을 해결하기 위하여 종래의 PI 제어기를 대체하여 사용한다. HAI 제어기는 지령모델 기반의 적응제어, 퍼지제어 및 신경회로망으로 구성되어 속도 성능을 개선한다. 본 논문에서는 제시한 HAI 제어기를 적용하여 파라미터 변동, 정상상태 및 과도상태 등의 응답특성을 분석하고 종래의 FNN 제어기 및 PI 제어기의 응답특성과 비교한다. 따라서 본 논문에서는 HAI 제어기의 타당성을 입증한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 춘계학술대회 논문집 에너지변화시스템부문
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• pp.225-227
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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 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.