• 조명전기설비학회논문지
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• 제25권7호
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• pp.15-25
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• 2011

This proposes an online loss minimization algorithm for series PI(SPI) based interior permanent magnet synchronous motor(IPMSM) drive to yield high efficiency and high dynamic performance over wide speed range. The loss minimization algorithm is developed based on the motor model. In order to minimize the controllable electrical losses of the motor and thereby maximize the operating efficiency, the d-axis armature current is controlled optimally according to the operating speed and load conditions. For vector control purpose, a SPI is used as a speed controller which enables the utilization of the reluctance torque to achieve high dynamic performance as well as to operate the motor over a wide speed range. Also, this paper proposes current control of model reference adaptive fuzzy controller(MFC), and estimation of speed using artificial neural network(ANN) controller. The proposed efficiency optimization control, SPI, MFC, ANN in this paper is applied to IPMSM drive system, the validity of this paper is proved by analyzing response characteristics in variety operating conditions.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1991년도 추계학술대회 논문집 학회본부
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• pp.140-143
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• 1991

This paper describes a compensation method for the rotor resistance variation of induction machines in speed sensor-less vector control system using MRAS(model reference adaptive system). In case of rotor resistance variation, the analysis of the conventional speed sensor-less vector control system using MRAS is presented and the compensation method for rotor resistance variation using Fuzzy logic is proposed. In order to confirm the performance of the proposed algorithm, computer simulation is performed.

• 한국지능시스템학회논문지
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• 제5권4호
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• pp.101-110
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• 1995

본 연구에서는 직류 서보전동기의 위치, 속도 및 토오크 등의 제어기법중 외란에 대해 강인한 것으로 평가되고 있는 기존의 가속도 제어기법을 해석하고 그 결과 적용상의 문제점으로 판단되는 계통 파라미터 변화에 대한 강인성을 개선하기 위한 대책으로서 변경된 가속도제어 루프를 포함한 위치제어계를 제안하고 이를 직류전동기의 위치제어게 적용하였다. 제안된 제어계는 가속도제어기와 자기종조 퍼지 PID 제어기로 구성되어 있으며 제안된 제어계와 기존의 가속도제어계와의 근복적 차이점은 이 제어기법의 핵심 요소라 할 수 있는 가속도 기준입력 정정량의 발생기구로서 고전적 PID제어기를 자동조정하는 자기동조 PID 제어방식을 채택하였다는 점이다. 이 기준입력 발생기구를 도입한 직류전동기 위치제어계의 성능을 검토하기 위하여 그 응답특성을 기존의 가속도제어 위치제어계의 응답특성과 비교한 결과 파라미터 변화에 대한 강인성, 과도 특성 및 정상상태 특성면에서 제안된 제어계가 매우 우월한 성능을 가짐을 확인하였다. 따라서 제안된 제어기법은 기존의 가속도제어에 근거한 위치제어계의 성능을 개선시켜줌으로써 이 제어기법의 적용범위 확대에 기여하며 특히 모델의 불확정성이 크거나 부하등 운전환경이 크게 변화하는 악조건 하에서의 정밀한 위치제어에 적용될 수 있다.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2003년도 추계학술대회 논문집
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• pp.186-189
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• 2003

This paper is proposed to position and speed control of interior permanent magnet synchronous motor(IPMSM) drive without mechanical sensor. A gopinath observer is used for the mechanical state estimation of the motor. The observer was developed based on nonlinear model of IPMSM, that employs a d-q rotating reference frame attached to the rotor, A gopinath observer is implemented to compute the speed and position feedback signal. The validity of the proposed scheme is confirmed by various response characteristics.

• Journal of Electrical Engineering and information Science
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• 제2권6호
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• pp.20-27
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• 1997

We present a method of electromyographic(EMG) pattern recognition to identify motion commands for the control of a prosthetic arm by evidence accumulation with multiple parameters. Integral absolute value, variance, autoregressive(AR) model coefficients, linear cepstrum coefficients, and adaptive cepstrum vector are extracted as feature parameters from several time segments of the EMG signals. Pattern recognition is carried out through the evidence accumulation procedure using the distances measured with reference parameters. A fuzzy mapping function is designed to transform the distances for the application of the evidence accumulation method. Results are presented to support the feasibility of the suggested approach for EMG pattern recognition.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.714-716
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• 1995

This Paper presents a novel and systematic approach to a self-learning controller. The proposed controller is built on a neural network consisting of a standard back propagation (BNN) and approxinate reasoning (AR). The fuzzy inference and knowledge representation are carried out by the neural network structure and computing, instead of logic inference. An architecture similar to that used by traditional model reference adaptive control system (MRAC) is employed.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권3호
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• pp.185-191
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• 2008

In this paper, experimental studies of a neural network (NN) control technique for non-model based position control of the x-y table robot are presented. Decentralized neural networks are used to control each axis of the x-y table robot separately. For an each neural network compensator, an inverse control technique is used. The neural network control technique called the reference compensation technique (RCT) is conceptually different from the existing neural controllers in that the NN controller compensates for uncertainties in the dynamical system by modifying desired trajectories. The back-propagation learning algorithm is developed in a real time DSP board for on-line learning. Practical real time position control experiments are conducted on the x-y table robot. Experimental results of using neural networks show more excellent position tracking than that of when PD controllers are used only.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1287-1292
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• 2005

This paper presents an effective pattern classification model by designing an artificial neural network based pattern classifiers for face recognition. First, a RGB image inputted from a frame grabber is converted into a HSV image which is similar to the human beings' vision system. Then, the coarse facial region is extracted using the hue(H) and saturation(S) components except intensity(V) component which is sensitive to the environmental illumination. Next, the fine facial region extraction process is performed by matching with the edge and gray based templates. To make a light-invariant and qualified facial image, histogram equalization and intensity compensation processing using illumination plane are performed. The finally extracted and enhanced facial images are used for training the pattern classification models. The proposed H-ART2 model which has the hierarchical ART2 layers and F-LVQ model which is optimized by fuzzy membership make it possible to classify facial patterns by optimizing relations of clusters and searching clustered reference patterns effectively. Experimental results show that the proposed face recognition system is as good as the SVM model which is famous for face recognition field in recognition rate and even better in classification speed. Moreover high recognition rate could be acquired by combining the proposed neural classification models.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2003년도 하계학술대회 논문집 D
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• pp.2618-2620
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• 2003

본 연구에서 구현하고자 하는 선박의 회두각 제어의 경우 파도, 바람, 조류 등의 외란의 영향을 많이 받고 있을 뿐만 아니라 그 운동 특성 역시 비선형이므로 적절한 파라미터의 선정과 제어기 구성에 어려움이 따른다. 이의 해결을 위해 K. M. Passino 등에 의해 비선형 특성을 지닌 기준 모델 적응 퍼지 알고리즘을 적용하여 제어기 구성을 시도한바 있고, 국내에서도 김종화 등에 의해 유사한 방법이 시도되어졌다. 본 연구에서는 이상의 시도에서 기준 모델에 의한 제어기 파라미터의 동정의 방법으로 사용한 M.I.T 룰 대신 일반적인 유전 알고리즘에 의해 퍼지 제어기의 파라미터를 동정하고자 한다. 유전 알고리즘에 기반한 기준 모델 적응 퍼지 제어기(MRGAFC) 알고리즘을 제안하며, 이의 검증을 위하여 화물선 회두각의 조향 문제에 이를 적용하여 종래의 방법들과 비교를 수행할 것이다.