• 전기학회논문지P
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• 제58권3호
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• pp.249-256
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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 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. The paper proposes high performance speed and current control of synchronous reluctance motor(SynRM) drive using adaptive learning 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, FLC and ANN controller.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2008년도 춘계학술대회 논문집
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• pp.440-444
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• 2008

The 7-phase BLDC motor is possible for higher efficiency per the unit area, high power and high speed due to the increasing number of phase. Also, it can be looking forward to reduce the current ripple at a point of commutation by the increasing number of phase. Thus, a study for applications of servo system, medical and military instruments is progressing about the BLDC motor is manufactured with multi-phase, currently. This paper is used the fuzzy logic control method for speed control of 7-phase BLDC motor and this is compared with the conventional PI controller using by simulation and experimental results for verification validity of the fuzzy logic controller in this system. The 7-phase BLDC motor and controller are modeled by PSIM6.0 software of PowerSim co. in simulation and we are experimented by the test board that is composed with TMS320VC33-150 DSP controller of Texas Instruments co. and FLEX EPF6016TC144-3 of ALTERA co.

• Journal of Power Electronics
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• 제11권4호
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• pp.393-400
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• 2011

The main objective of this paper is to control the speed of Nonlinear Hybrid Electric Vehicle (HEV) by controlling the throttle position. Various control techniques such as well known Proportional-Integral-Derivative (PID) controller in conjunction with state feedback controller (SFC) such as Pole Placement Technique (PPT), Observer Based Controller (OBC) and Linear Quadratic Regulator (LQR) Controller are designed. Some Intelligent control techniques e.g. fuzzy logic PD, Fuzzy logic PI along with Adaptive Controller such as Self Organizing Controller (SOC) is also designed. The design objective in this research paper is to provide smooth throttle movement, zero steady-state speed error, and to maintain a Selected Vehicle (SV) speed. A comparative study is carried out in order to identify the superiority of optimal control technique so as to get improved fuel economy, reduced pollution, improved driving safety and reduced manufacturing costs.

• 한국항공우주학회지
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• 제40권5호
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• pp.431-438
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• 2012

고기동 무인항공기용 터보제트엔진의 고성능 운용을 위한 비선형 제어기를 설계하였다. 제트엔진의 비선형특성을 제어기 설계에 반영하기 위해 당해엔진의 동적해석코드를 개발하였고 이를 이용한 선형모델로부터 선형해석의 한계성을 검증함으로써 비선형 제어기 설계의 당위성을 보였다. PI형 퍼지제어기를 일반 미분제어기와 결합하여 효율적인 퍼지제어기를 설계하였으며, 일반 PID제어기와 비교하여 압축기서지 및 연소정지영역, 터빈온도의 제한 등 안전운전 범위 내 운전을 보장하면서도 월등한 제어성능을 보임으로써 설계된 퍼지제어기의 유용성을 입증하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1997년도 하계학술대회 논문집 B
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• pp.606-608
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• 1997

In this paper, Neuro-Fuzzy Controller(NFC), a fuzzy system realized using a neural network, is to adopt for the multivariable system. In the multivariable system, the interactive effects between the variables should be taken into account. A simple compensator, using the steady-state information can be obtained for open-loop stable systems, is presented to cope with this problem. However, it should be supposed that the plant is unknown to the control system designer, but an estimate of the DC gain has been obtained by carrying out experiments on the plant. Also, if the variables are not combinated completely, it is difficult to design the controller. Therefore, we design a neuro-fuzzy controller which controls a multivariable system with only input output informations, and compare its performance with that of a PI controller. In the proposed controller, the construction of the membership functions and rule base, which is highly heuristic, can be achieved using a training process. This allows the combination of knowledge of human experts and evidence from input-output data.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 춘계학술대회 논문집 에너지변화시스템부문
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• pp.32-34
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• 2009

The field oriented control of induction motors is widely used in high performance applications. However, detuning caused by parameter disturbance still limits the performance of these drives. In order to accomplish variable speed operation, conventional PI-like controllers are commonly used. These controllers provide limited good Performance over a wide range of operation, even under ideal field oriented conditions. This paper is proposed model reference adaptive fuzzy control(MFC) and artificial neural network(ANN) based on the vector controlled induction motor drive system. Also, this paper is proposed control of speed and current using fuzzy adaption mechanism(FAM), MFC and estimation of speed using ANN. The proposed control algorithm is applied to induction motor drive system using FAM, MFC and ANN controller. Also, this paper is proposed the analysis results to verify the effectiveness of this 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.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 2009년도 춘계학술대회 논문집
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• pp.404-407
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• 2009

The field oriented control of induction motors is widely used in high performance applications. However, detuning caused by parameter disturbance still limits the performance of these drives. In order to accomplish variable speed operation conventional PI-like controllers are commonly used. These controllers provide limited good performance over a wide range of operation even under ideal field oriented conditions. This paper is proposed adaptive fuzzy controller(AFC) and artificial neural network(ANN) based on the vector controlled induction motor drive system. Also, this paper is proposed control of speed and current using fuzzy adaptation mechanism(FAM), AFC and estimation of speed using ANN. The proposed control algorithm is applied to induction motor drive system using FAM, AFC and ANN controller. Also, this paper is proposed the analysis results to verify the effectiveness of this controller.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2019년도 학술발표회
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• pp.392-392
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• 2019

현재 세계적으로, 홍수를 비롯한 자연재해로 인한 피해가 증가하고 있다. 우리나라의 경우, 매년 여름철에 발생하고 있는 장마로 인해 지역 곳곳에 침수피해가 심각해지고 있으며, 이에 대한 피해액 또한 증가하고 있는 실정이다. 또한, 여러 재해의 피해반복과 새롭게 반복되는 건축물 설계로 인해 지형이 바뀌고 있으며, 이로 인해, 기존의 실시된 홍수취약성 분석결과가 현실적으로 반영이 되기 힘든 상태이다. 피해를 줄이기 위해서는 변형된 환경에 맞춰 새로운 홍수취약성 분석을 실시하여 지역의 우선순위를 파악하여야 한다. 본 연구에서는 우리나라중 인구와 건물밀집도가 가장 높은 서울시 25개 구를 대상지역으로 선정하였으며, 인자들을 Pessure-State-Response (PSR) 구조로 나누었다. 압력지수(PI) 에는 유역면적, 주택 수 등 9개의 인자로, 상태지수(SI)는 연 홍수 피해액 등 4개의 인자로 선정하였으며, 대책지수(RI)의 경우에는 재정자립도, 홍수복구금액등 7개의 인자로 나누었다. 분석방법으로는 의사결정과정에서 발생할 수 있는 불확실성을 정량적으로 반영한 AHP방법과 AHP방법에 Fuzzy이론을 결합한 Fuzzy AHP 방법을 통해 각각의 결과를 비교분석하였다. 그 결과, 3개의 지수 모두 인자들의 지역별 취약순위가 바뀌었다. 본 연구의 결과를 바탕으로 홍수 방재 관련 정책 수립 등의 사업 등을 실시할 경우 해당지역에 대한 우선순위를 판단하는데 도움이 될 것으로 판단된다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2008년도 제39회 하계학술대회
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• pp.1718-1719
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• 2008

본 논문에서는 철도차량의 능동조향을 위한 고전 PI 제어기 및 퍼지 제어기를 설계하여 그 성능을 서로 비교하였다. 철도차량에서 능동조향은 곡선부 주행 시 발생되는 승차감 저하 및 차륜/레일의 마모와 소음을 줄이고, 고속주행을 위한 조향성능 및 주행안정성을 확보하기 위한 제어기술이다. 논문에서는 차량 1량을 모델로 하여 측정된 휠-레일의 횡변위(Lateral Displacement) 정보를 토대로 휠의 요모멘트를 제어하는 전략을 사용하여 제어기를 설계하였으며, 시뮬레이션을 통해 제어기 응답 특성을 비교하였다.