• 한국전자통신학회논문지
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• 제3권4호
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• pp.266-275
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• 2008

본 논문에서는 전동기 파라미터와 부하에 강인한 속도 특성을 위해 디지털 IP제어를 이용한 BLDC 전동기의 센서리스 속도제어 방식을 제안한다. 단자 전압을 이용한 BLDC 전동기의 센서리스 구동시 회전자 위치 추정을 위해 아날로그 필터를 사용하기 때문에 부하나 속도에 영향을 받는다. 전동기 파라미터에 둔감하고 부하의 영향에도 강건한 센서리스 속도제어를 하기 위해서는 정확한 회전자 위치 추정과 연동하는 강인한 속도 제어기가 필요하다. 본 논문에서는 디지털 IP제어를 이용하여 부하의 변동에도 강인한 정속제어와 일정 부하로 운전 중 가감속시 임의의 속도변화에 대해 BLDC 전동기의 안정된 센서리스 제어가 가능하도록 구성하였다. 이에 대한 타당성은 실험을 통하여 입증하고자 한다.

• 디지털융복합연구
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• 제10권10호
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• pp.277-282
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• 2012

음성 인식 시스템은 다양하게 변화하는 환경 잡음에 빠르게 적응할 수 없어서 인식 성능을 저하시키는 요인이 된다. 본 논문에서는 평균 예측 LMS 알고리즘을 이용하여 반향 잡음에 강인하게 하는 방법으로 HMM 학습 모델을 구성하는 방법을 제안하였으며, 변화하는 반향 잡음에 적응하도록 HMM 학습 모델을 구성하여 인식 성능을 평가하였다. 실험 결과 변화하는 환경 잡음을 제거하여 얻은 음성의 SNR은 평균 3.1dB이 향상되었고 인식률은 3.9% 향상되었다.

• 전력전자학회:학술대회논문집
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• 전력전자학회 2001년도 Proceedings ICPE 01 2001 International Conference on Power Electronics
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• pp.443-447
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• 2001

In this paper, a robust sensorless vector control system for induction motors with a speed estimator and an uncertainty observer is presented. At first, the proposed speed estimator is based on the MRAS(Mode Reference Adaptive System) scheme and constructed with a simple fuzzy logic(FL) approach. The structure of the proposed FL estimator is very simple. The input of the FL is the rotor flux error difference between reference and adjustable model, and the output is the estimated incremental rotor speed Secondly, the unmodeled uncertainties such as parametric uncertainties and external load disturbances are modeled by a radial basis function network(RBFN). In the overal speed control system, the control inputs are composed with a norminal control input and a compensated control input, which are from RBFN observer output and the modeling error of the RBFN, repectively. The compensated control input is derived from Lyapunov unction approach. The simulation results are presented to show the validity of the proposed system.

• International Journal of Precision Engineering and Manufacturing
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• 제5권4호
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• pp.14-20
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• 2004

In this work, a new control methodology to achieve accurate position control of an AC servo motor subjected to external disturbance is proposed. Unlike conventional sliding mode controller which requires a prior knowledge of the upper bound of external disturbance, the proposed technique, called sliding mode controller with disturbance estimator (SMCDE), can offer robust control performances without a prior knowledge of the disturbance bound. The SMCDE is featured by an integrated average value of the imposed disturbance over a certain sampling period. By doing this, undesirable chattering phenomenon in the estimation process can be effectively alleviated. The benefits of the proposed control methodology are empirically demonstrated on AC servo motor and control responses are evaluated through a comparative work between the proposed and conventional control schemes.

• Journal of the Korean Statistical Society
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• 제27권3호
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• pp.369-383
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• 1998

This paper deals with a robust regression estimator. We propose an efficient one-step GM-estimator, which has a bounded influence function and a high breakdown point. The main idea of this paper is to use the Mallows-type weights which depend on both the predictor variables and the residuals from a high breakdown initial estimator. The proposed weighting scheme severely downweights the bad leverage points and slightly downweights the good leverage points. Under some regularity conditions, we compute the finite-sample breakdown point and prove the asymptotic normality. Some simulation results and a numerical example are also presented.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1988년도 추계학술대회 논문집 학회본부
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• pp.80-83
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• 1988

In this paper a robust control law is presented which stabilezes overall system via pole reassignment and loop-shaping. A robust adaptive controller is designed combining this robust control law and a robust estimator.

• Communications for Statistical Applications and Methods
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• 제6권2호
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• pp.345-356
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• 1999

This paper deals with the problem of estimating regression coefficients in nonlinear regression model having random regressor. The sufficient conditions for consistency of the $L_1$-estimator with random regressor are given and discussed in this paper. An example is given to illustrate the application of the main results.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.419-424
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• 1992

A robust nonlinear predictive control strategy using a disturbance estimator is presented. The disturbance estimator is comprised of two parts: one is the disturbance model parameter adaptation and the other is future disturbance prediction. RLSM(recurrsive least square method) with a forgetting factor is used to de the uncertain distance model parameters and for the future disturbance prediction, future process outputs and inputs projected by the process model are used. The simulation results for chemical reactors indicate that a substantial improvement in nonlinear predictive control performance is possible using the disturbance estimator.

• Journal of the Korean Statistical Society
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• 제30권4호
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• pp.551-561
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• 2001

This paper deals with the asymptotic properties for statistical inferences of the parameters in nonlinear regression models. As an optimal criterion for robust estimators of the regression parameters, the regression quantile method is proposed. This paper defines the regression quintile estimators in the nonlinear models and provides simple and practical sufficient conditions for the asymptotic normality of the proposed estimators when the parameter space is compact. The efficiency of the proposed estimator is especially well compared with least squares estimator, least absolute deviation estimator under asymmetric error distribution.

• Journal of the Korean Statistical Society
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• 제24권2호
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• pp.349-360
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• 1995

This paper deals with the robustness properties of the minimum disparity estimation in linear regression models. The estimators defined as statistical quantities whcih minimize the blended weight Hellinger distance between a weighted kernel density estimator of the residuals and a smoothed model density of the residuals. It is shown that if the weights of the density estimator are appropriately chosen, the estimates of the regression parameters are robust.