• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.2374-2376
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• 2000

In this paper, we propose an indirect vector control method using Adaptive Neuro-Fuzzy Inference System (ANFIS) parameter estimator. It estimates the rotor time constant when the indirect vector control of induction motor is applied. We use the stator current error that is difference between the current command and estimated current calculated from terminal voltage and current. And two induced current estimate equations are used in training ANFIS.The estimator is trained by the hybrid learning algorithm. Simulation results shows good performance under load disturbance and motor parameter variations.

• 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.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2021년도 추계학술대회
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• pp.606-608
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• 2021

사물인터넷을 이용하여 인간과 다양한 기기가 상호 연결되어 다양하게 활용되고 있다. 본 연구에서는 ICT를 이용하여 원격지와의 동기 제어시스템을 구축하고 이를 활용한 보행자용 신호시스템을 구현하였다. 제어기 구현을 위해서 PLC 회로를 활용하였으며, 이를 통해 무선 제어신호 송신 및 수신, LED 램프 발광제어, 전원제어 등을 수행하였다. 제어기 및 보행자 전용 신호등 시스템 구축을 통해서 메인 제어기로부터 원격지의 서브 제어기와의 신호 동기가 가능함을 확인하였고, 도로 양쪽에 설치된 보행신호 등 시스템의 램프 신호 동기화를 구현하였다. 이 결과로 1:1 원격제어, 혹은 1:N의 원격 그룹제어가 가능함을 확인하였으며, 이 결과물은 다양한 분야에 활용될 수 있다.

• 전기학회논문지P
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• 제56권3호
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• pp.129-135
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• 2007

This paper deals with speed-sensorless control of DC servo motor using Neuro-Fuzzy Observer. DC servo motor has very low rotor inertia and excellent response characteristic and it is very useful to control torque and speed. It is easy to detect the voltage and current and resolver or encoder is used to measure a rotor speed. But it has a limit as a driving speed to detect speed precisely. So it is problem to improve the performance of the driving system. To solve this problem, it is studied to detect a speed of DC servo motor without sensor. In particular, study on the method to estimate the speed using the observer is performed a lot. In this paper, the gain of the observer is properly set up using the Neuro-Fuzzy control and Neuro-Fuzzy Observer that have a superior transient characteristic and is easy to implement compared the existing method is designed. It calculates the differentiation of the rotor current directly using the rotor current measured in the DC servo motor and estimates the speed of the rotor using the differentiation. Proposed speed sensorless control method is performed using the estimated speed. Also, it is proved feasibility of the proposed observer from the comparison tested a case with a speed sensor and a case without a speed sensor which used a highly efficient drive and 200[w] DC servo motor starting system.

• 대한전기학회논문지:시스템및제어부문D
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• 제54권5호
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• pp.285-290
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• 2005

The state observer is being used widely because it has the advantage of the guarantee of reliability on financial problem, over heating, and physical shock. However, an Luenberger observer and a Sliding observer have such problems that an experimenter needs to know dynamics and parameters of the system. And also, the high gain observer has such a problem that it has transient state at the beginning of the observation. In this paper, the Neuro observer is proposed to improve these problems. The proposed Neuro observer complement a problem that occur from increase of gain of High-gain observer in proportion to the square number of observable state variables. And also, the proposed Neuro observer can tune the gain obtained by differentiating observational error at transient state automatically by using the backpropagation training method to stabilize the observational speed. To prove a performance of the proposed observer, it is simulated that the comparison between the state estimate performance of the proposed observer and that of Sliding, High gain observer is made by using a sinusoidal input to the observer which consists of four layers in stable 2nd order system.

• 한국통신학회논문지
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• 제18권5호
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• pp.611-618
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• 1993

디지털 통신 시스템에서는 미리 정해진 모양의 파형을 발생시키는 것이 필수적이다. 이를 위하여 본 논문에서는 방송계 뉴 미디어 시스템인 RDS(Radio Data System)의 부호화기에서 파형 정형의 한 방법으로 메모리의 Look-up 테이블을 이용하여 기존의 아날로그 여파기보다 효율적인 디지털 파형 여파기를 구현하는 한편, 수신단에서는 전송한 데이터가 에러없이 정확히 복원 가능함을 보인다. 디지털 파형 여파기를 구현하기 위하여 파형 여파기의 제어 알고리즘을 설계하고, IC화를 목표로 discrete 소자를 이용하여 구현하였다. 또한 RDS의 송수신단 전송 실험을 통하여 본 논문에서 제안한 디지틀 파형 여파기의 타당성을 입증하였다.

• 대한전기학회논문지:전력기술부문A
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• 제49권3호
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• pp.95-106
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• 2000

The load frequency control of power system is one of important subjects in view of system operation and control. That is even though the rapid load disturbances were applied to the given power system, the stable and reliable power should be supplied to the users, converging unconditionally and rapidly the frequency deviations and the tie-line power flow one on each area into allowable boundary limits. Nonetheless of such needs, if the internal parameter perturbation and the sudden load variation were given, the unstable phenomenal of power system can be often brought out because of the large frequency deviation and the unsuppressible power line one. Therefore, it is desirable to design the robust neuro-fuzzy controller which can stabilize effectively the given power system as soon as possible. In this paper the robust neuro-fuzzy controller was proposed and applied to control of load frequency over multi-area power system. The architecture and algorithm of a designed NFC(Neuro-Fuzzy Controller) were consist of fuzzy controller and neural network for auto tuning of fuzzy controller. The adaptively learned antecedent and consequent parameters of membership functions in fuzzy controller were acquired from the steepest gradient method for error-back propagation algorithm. The performances of the resultant NFC, that is, the steady-state deviations of frequency and tie-line power flow and the related dynamics, were investigated and analyzed in detail by being applied to the load frequency control of multi-area power system, when the perturbations of predetermined internal parameters. Through the simulation results tried variously in this paper for disturbances of internal parameters and external stepwise load stepwise load changes, the superiorities of the proposed NFC in robustness and adaptive rapidity to the conventional controllers were proved.

• 한국추진공학회지
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• 제25권5호
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• pp.36-52
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• 2021

뉴 스페이스 시대를 맞아 군집 초소형 위성의 활용이 전 세계적으로 증가함에 따라, 위성의 정밀제어를 위한 추력기가 필수적으로 요구되고 있다. 전계방출 전기추진(Field Emission Electric Propulsion, FEEP) 추력기는 추진제로 액체 금속을 사용하는데, 강한 전기장에 의해 이온화된 액체금속을 가속시키는 방식의 추력기이다. FEEP 추력기는 1 µN급에서 1 mN급까지의 추력 범위와 10,000 s 수준에 이르는 큰 비추력을 가지며, 구조가 단순하고 소형화가 가능하여 초소형 위성의 다양한 자세 및 궤도 제어 임무에 적합하다. 본 논문에서는 FEEP 추력기의 개요를 소개하고, 연구개발 현황에 대해서 살펴보고자 한다.

• 전기학회논문지
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• 제60권9호
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• pp.1700-1707
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• 2011

Interior permanent magnet synchronous motor(IPMSM) adjustable speed drives offer significant advantages over induction motor drives in a wide variety of industrial applications such as high power density, high efficiency, improved dynamic performance and reliability. Since the fuzzy neural network(FNN) is recognized general approximate method to control non-linearities and uncertainties, the development of FNN control systems have also grown rapidly. The FNN controller is compounded of fuzzy and neural network. It has an advantage that is the robustness of fuzzy control and the ability to adapt of neural network. However, the FNN has static problem due to their feed-forward network structure. This paper proposes high performance speed control of IPMSM drive using the recurrent FNN(RFNN) which improved conventional FNN controller. The RFNN has excellent dynamic response characteristics because of it has internally feed-back structure. Also, this paper proposes speed estimation of IPMSM drive using ANN. The proposed method is analyzed and compared to conventional FNN controller in various operating condition such as parameter variation, steady and transient states etc.

• 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.