• Journal of Power Electronics
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• 제18권5호
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• pp.1380-1397
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• 2018

The synchronous reluctance motor (SynRM) servo-drive system has highly nonlinear uncertainties owing to a convex construction effect. It is difficult for the linear control method to achieve good performance for the SynRM drive system. The nonlinear backstepping control system using upper bound with switching function is proposed to inhibit uncertainty action for controlling the SynRM drive system. However, this method uses a large upper bound with a switching function, which results in a large chattering. In order to reduce this chattering, a nonlinear backstepping control system using an adaptive law is proposed to estimate the lumped uncertainty. Since this method uses an adaptive law, it cannot achiever satisfactory performance. Therefore, a nonlinear backstepping control system using a reformed recurrent Hermite polynomial neural network with an adaptive law and an error estimated law is proposed to estimate the lumped uncertainty and to compensate the estimated error in order to enhance the robustness of the SynRM drive system. Further, the reformed recurrent Hermite polynomial neural network with two learning rates is derived according to an increment type Lyapunov function to speed-up the parameter convergence. Finally, some experimental results and a comparative analysis are presented to verify that the proposed control system has better control performance for controlling SynRM drive systems.

• 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.

• 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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.257-259
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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.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2009년도 춘계학술대회 논문집
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• pp.416-419
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• 2009

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 loaming 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. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive loaming mechanism-fuzzy neural network (ALM-FNN) and fuzzy logic control(FLC) controller. The proposed controller is developed to ensure accurate speed and current control of SynRM drive under system disturbances and estimation of speed using artificial neural network(ANN) controller. Also, this paper proposes the analysis results to verify the effectiveness of the ALM-FNN and ANN controller.

• Journal of Power Electronics
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• 제6권4호
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• pp.340-346
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• 2006

A high efficiency drive technique for synchronous reluctance motors (SynRM) using a neural network (NN) is presented in this paper. High efficiency drive condition depends on the mathematical model of SynRM. A NN is employed as an adaptive model of SynRM. The proposed high efficiency drive technique does not require an accurate mathematical model of SynRM. Moreover, the proposed method shows robustness against machine parameter variations because the training algorithm of the NN is executed on-line. The usefulness of the proposed method is confirmed through experimentation.

• 조명전기설비학회논문지
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• 제24권5호
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• pp.44-54
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• 2010

SynRM 효율최적화 제어는 다른 교류전동기에 비해 SynRM의 효율이 낮기 때문에 에너지 절약과 환경보존의 관점에서 매우 중요하다. 본 논문에서는 다중 AFLC를 이용하여 철손을 고려한 SynRM의 새로운 효율 최적화 제어를 제안하였다. 최대효율에서 SynRM을 구동하기 위해 토크전류와 여자전류사이의 최적전류비를 분석하여 구한다. 본 논문에서는 동손과 철손을 최소로 하는 SynRM의 효율 최적화 제어를 제안하였다. 특정한 모터토크를 제공하는 d축과 q축 전류의 다양한 조합이 존재한다. 효율 최적화의 목적은 정상상태에서 최소 손실을 제공하는 d축과 q축 전류의 조합을 찾는 것이며, 제안된 제어기의 제어 성능은 다양한 동작조건의 분석을 통해 평가되었다. 분석된 결과는 제안된 알고리즘의 타당성을 입증한다.

• 조명전기설비학회논문지
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• 제20권10호
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• pp.47-57
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• 2006

본 논문은 ALM-FNN 제어기와 ANN 제어기를 사용하여 SynRM 드라이브의 최대토크 제어를 제시한다. 이 제어기는 인버터의 정격 전류와 전압 제한을 고려하고 전 속도 영역에 적용된다. 각 제어모드를 위하여 최대토크를 위한 최적의 d-축 전류 $^i{_d}$를 결정한다. 제시된 제어 알고리즘은 ALM-FNN 제어기와 ANN 제어기로 SynRM 드라이브 시스템을 제어하는데 적용된다. 최대토크 제어에 의하여 제어된 동작 특성은 실험을 통하여 상세히 설명한다. 또한 본 눈문은 ALM-FNN 제어기와 ANN 제어기 결과분석을 통하여 타당성을 입증한다.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2009년도 추계학술대회 논문집
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• pp.359-362
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• 2009

Optimal efficiency control of synchronous reluctance motor(SynRM) is very important in the sense of energy saving and conservation of natural environment because the efficiency of the SynRM is generally lower than that of other types of AC motors. This paper is proposed a novel efficiency optimization control of SynRM considering iron loss using multi adaptive fuzzy learning controller(AFLC). The optimal current ratio between torque current and exciting current is analytically derived to drive SynRM at maximum efficiency. This paper is proposed an efficiency optimization control for the SynRM which minimizes the copper and iron losses. There exists a variety of combinations of d and q-axis current which provide a specific motor torque. The objective of the efficiency optimization control is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. The control performance of the proposed controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2005년도 학술대회 논문집
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• pp.309-314
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• 2005

The paper is proposed maximum torque control of SynRM drive using learming mechanism-fuzzy neural network(LM-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 LM-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 LM-FNN and ANN controller.