• Journal of Electrical Engineering and Technology
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• 제5권1호
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• pp.129-139
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• 2010

To be satisfied with complex load condition of electric vehicle, fuzzy logic control (FLC) is applied to improve speed response and system robust performance of induction traction machine based on indirect rotor field orientation control. The proposed propulsion system consists of two induction motors (IM) that ensure the drive of the two back driving wheels of lightweight electric vehicle by means the vehicle used for passenger transportation. The electronic differential system ensures the robust control of the vehicle behavior on the road. It also allows controlling, independently, every driving wheel to turn at different speeds in any curve. Our electric vehicle fuzzy inference system control's simulated in Matlab SIMULINK environment, the results obtained present the efficiency and the robustness of the proposed control with good performances compared with the traditional PI speed control, the FLC induction traction machine presents not only good steady characteristic, but with no overshoot too.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2004년도 춘계학술대회 논문집 전기기기 및 에너지변환시스템부문
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• pp.143-146
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• 2004

This paper is proposed adaptive fuzzy-neural network(FNN) controller for speed control of induction motor drive. The design of this algorithm based on FNN controller that is implemented using fuzzy control and neural network. A model reference adaptive scheme is proposed in which the adaptation mechanism is executed by fuzzy logic based on the error and change of error measured between the motor speed and output of a reference model. The control performance of the adaptive FNN controller is evaluated by analysis for various operating conditions.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2009년도 제40회 하계학술대회
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• pp.778_779
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• 2009

The maximum output torque developed by the machine is dependent on the allowable current rating and maximum voltage that the inverter can supply to the machine. Therefore, to use the inverter capacity fully, it is desirable to use the control scheme considering the voltage and current limit condition, which can yield the maximum torque per ampere over the entire speed range. The paper is proposed maximum torque control of induction motor drive using adaptive learning neuro fuzzy 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, q axis current $_i_{ds}$, $i_{qs}$ for maximum torque operation is derived. The proposed control algorithm is applied to induction motor drive system controlled adaptive learning neuro fuzzy controller 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 adaptive learning neuro fuzzy controller and ANN controller.

• 대한전기학회논문지:시스템및제어부문D
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• 제50권7호
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• pp.318-324
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• 2001

Efficiency optimization of an indirect vector controlled induction motor drive is proposed. The loss models are used in the validation of the fuzzy logic based on-line efficiency optimization control. At steady state, the fuzzy controller adaptively changes the excitation current on the basis of measured input power, until the maximum efficiency point is reached. The pulsating torque, due to flux reduction, has been compensated by an ingenious feedforward scheme. During transient state, rated flux is established to get the best transient response. Through a comprehensive simulation study, the results confirmed the validity of the proposed method.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2010년도 하계학술대회 논문집
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• pp.585-586
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• 2010

An induction motor operated with a conventional direct self controller(DSC) shows a sluggish response during startup and under changes of torque command. Fuzzy logic controller(FLC) is used in conjunction with DSC to minimize these problems. A FLC chooses the switching states based on a set of fuzzy variables. Flux position, error in flux magnitude and error in torque are used as fuzzy state variables. Fuzzy rules are determinated by observing the vector diagram of flux and currents. This paper proposes hybrid FLC for direct torque control(DTC) of induction motor drives. This controller is controlled speed using hybrid FLC. The performance of the proposed induction motor drive with hybrid FLC is verified by analysis results at various operation conditions.

• 조명전기설비학회논문지
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• 제23권9호
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• pp.24-30
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• 2009

본 논문은 유도전동기 구동을 위한 태양광 발전시스템의 퍼지기반 MPPT 제어를 제시한다. 태양광 발전시스템의 최대 출력을 위하여 퍼지제어기를 이용한다. 퍼지제어기는 입력으로 최대 출력과 실제출력의 오차와 오차변화분을 사용하고 제어변수로 전압 변화분을 출력한다. 태양광 발전시스템은 퍼지기반 MPPT를 이용하여 유도전동기를 구동한다. 본 논문에서 제시한 퍼지기반 MPPT 제어는 종래의 PI 제어기와 비교를 통해 MPPT 추적성능 및 드라이브 구동성능 비교를 통해 타당성을 입증한다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 A
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• pp.331-333
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• 1995

A high performance motor drive system must have a good speed command tracking, a insensitivity to a parameter variation and sampling time. In this paper, a robust speed controller for an induction motor is proposed. The speed controller is fuzzy-sliding adaptive controller and its system continuously is varied. That is, only P gain act in dynamic state, I gain in steady-state. Because this system is a sort of adaptive control system, global stability analysis is used to Lyapunov function. Consequently, in this paper application of fuzzy sliding adaptive controller to induction motor controlled by vecter control is presented and the control system is digitally implemented within DSP.

• 전력전자학회논문지
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• 제11권3호
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• pp.224-232
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• 2006

본 논문은 적응 퍼지 제어기에 의한 유도전동기의 ANN 센서리스 제어를 제시한다. 또한 AFC를 사용하여 속도를 제어하고 ANN 제어기를 이용하여 속도를 추정한다. 신경회로망의 역전파 알고리즘은 전동기 속도의 실시간 추정에 사용된다. 요구상태 변수와 실제 상태는 실제 상태 변수는 요구값에 일치하기 위해서 역전파 알고리즘에 의해 회전자 속도를 조절한다. 제시된 제어 알고리즘 AFLC와 ANN 제어기는 유도전동기 드라이브 시스템 제어에 적용된다. 그리고 본 논문은 AFLC와 ANN 제어기의 우수한 결과를 나타낸다.

• 조명전기설비학회논문지
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• 제19권8호
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• pp.48-55
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• 2005

본 논문은 약계자 영역에서 퍼지제어기를 이용하는 스핀들 유도전동기의 속도제어 방법을 제안한다. 제안된 퍼지제어기 기반의 스핀들 유도전동기의 성능은 기존의 PI 제어기를 기반으로 한 드라이브 구동 시스템과 비교되었다. 퍼지제어기를 사용하는 구동 드라이브 시스템은 기존의 PI 제어기를 사용하는 스핀들 유도전동기 드라이브 시스템보다 고성능 운전이 가능함을 시뮬레이션 및 실험결과로 확인하였다.

• 전기학회논문지P
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• 제54권2호
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• pp.73-79
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• 2005

This paper is proposed hybrid artificial intelligent (HAI) controller based on the vector controlled induction motor drive system. The hybrid combination of fuzzy control and neural network will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed estimation of induction motor using a closed-loop state observer. The rotor position is calculated through the stator flux position and an estimated flux value of rotation reference frame. A closed-loop state observer is implemented to compute the speed feedback signal. The results of analysis prove that the proposed control system has strong robustness to rotor parameter variation, and has good steady-state accuracy and transitory response.