• Title/Summary/Keyword: Adaptive Error Estimator

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A new adaptive mesh refinement strategy based on a probabilistic error estimation

  • Ziaei, H.;Moslemi, H.
    • Structural Engineering and Mechanics
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    • v.74 no.4
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    • pp.547-557
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    • 2020
  • In this paper, an automatic adaptive mesh refinement procedure is presented for two-dimensional problems on the basis of a new probabilistic error estimator. First-order perturbation theory is employed to determine the lower and upper bounds of the structural displacements and stresses considering uncertainties in geometric sizes, material properties and loading conditions. A new probabilistic error estimator is proposed to reduce the mesh dependency of the responses dispersion. The suggested error estimator neglects the refinement at the critical points with stress concentration. Therefore, the proposed strategy is combined with the classic adaptive mesh refinement to achieve an optimal mesh refined properly in regions with either high gradients or high dispersion of the responses. Several numerical examples are illustrated to demonstrate the efficiency, accuracy and robustness of the proposed computational algorithm and the results are compared with the classic adaptive mesh refinement strategy described in the literature.

Robust Control for Nonlinear Friction Servo System Using Fuzzy Neural Network and Robust Friction State Observer (퍼지신경망과 강인한 마찰 상태 관측기를 이용한 비선형 마찰 서보시스템에 대한 강인 제어)

  • Han, Seong-Ik
    • Journal of the Korean Society for Precision Engineering
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    • v.25 no.12
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    • pp.89-99
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    • 2008
  • In this paper, the position tracking control problem of the servo system with nonlinear dynamic friction is issued. The nonlinear dynamic friction contains a directly immeasurable friction state variable and the uncertainty caused by incomplete parameter modeling and its variations. In order to provide the efficient solution to these control problems, we propose the composite control scheme, which consists of the robust friction state observer, the FNN approximator and the approximation error estimator with sliding mode control. In first, the sliding mode controller and the robust friction state observer is designed to estimate the unknown internal state of the LuGre friction model. Next, the FNN estimator is adopted to approximate the unknown lumped friction uncertainty. Finally, the adaptive approximation error estimator is designed to compensate the approximation error of the FNN estimator. Some simulations and experiments on the servo system assembled with ball-screw and DC servo motor are presented. Results show the remarkable performance of the proposed control scheme. The robust friction state observer can successfully identify immeasurable friction state and the FNN estimator and adaptive approximation error estimator give the robustness to the proposed control scheme against the uncertainty of the friction parameters.

Adaptive L-estimation for regression slope under asymmetric error distributions (비대칭 오차모형하에서의 회귀기울기에 대한 적합된 L-추정법)

  • 한상문
    • The Korean Journal of Applied Statistics
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    • v.6 no.1
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    • pp.79-93
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    • 1993
  • We consider adaptive L-estimation of estimating slope parameter in regression model. The proposed estimator is simple extension of trimmed least squares estimator proposed by ruppert and carroll. The efficiency of the proposed estimator is especially well compared with usual least squares estimator, least absolute value estimator, and M-estimators designed for asymmetric distributions under asymmetric error distributions.

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Sensorless Speed Control of Induction motor using the Intelligent Speed Estimator (지능형 속도 추정기를 이용한 유도전동기의 센서리스 속도제어)

  • Park, Jin-Su;Choi, Sung-Dae;Kim, Sang-Hoon;Yoon, Kwang-Ho;Ban, Gi-Jong;Nam, Moon-Hyon;Kim, Lark-Kyo
    • Proceedings of the KIEE Conference
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    • 2004.11c
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    • pp.660-662
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    • 2004
  • This paper proposes an Intelligent Speed Estimator in order to realize the speed-sensorless vector control of an induction motor. Intelligent Speed Estimator used Model Reference Adaptive System which has Fuzzy-Neural adaptive mechanism as Speed Estimation method. The Intelligent Speed Estimator estimates the speed of an induction motor with a rotor flux of a reference model and adjustable model in MRAS. The Intelligent Speed Estimator reduces the error of the rotor flux between the voltage flux model and the current flux model using the error and the change of error as input of the Estimator. The computer simulation is executed to verify the propriety and the effectiveness of the proposed speed estimator.

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Design of Intelligent Speed Estimator for Speed Sensorless Control of Induction Motor (유도전동기의 속도 센서리스 제어를 위한 지능형 속도 추정기의 설계)

  • Park, Jin-Su;Choi, Sung-Dae;Kim, Sang-Hoon;Ko, Bong-Woon;Nam, Moon-Hyon;Kim, Lark-Kyo
    • Proceedings of the KIEE Conference
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    • 2004.07d
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    • pp.2304-2306
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    • 2004
  • This paper proposes an Intelligent Speed Estimator in order to realize the speed-sensorless vector control of an induction motor. Intelligent Speed Estimator used Model Reference Adaptive System which has Fuzzy-Neural adaptive mechanism as Speed Estimation method. The Intelligent Speed Estimator estimates the speed of an induction motor with a rotor flux of a reference model and adjustable model in MRAS. The Intelligent Speed Estimator reduces the error of the rotor flux between the voltage flux model and the current flux model using the error and the change of error as input of the Estimator. The computer simulation is executed to verify the propriety and the effectiveness of the proposed speed estimator.

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Adaptive Robust Regression for Censored Data (중도 절단된 자료에 대한 적은 로버스트 회귀)

  • Kim, Chul-Ki
    • Journal of Korean Society for Quality Management
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    • v.27 no.2
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    • pp.112-125
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    • 1999
  • In a robust regression model, it is typically assumed that the errors are normally distributed. However, what if the error distribution is deviated from the normality and the response variables are not completely observable due to censoring? For complete data, Kim and Lai(1998) suggested a new adaptive M-estimator with an asymptotically efficient score function. The adaptive M-estimator is based on using B-splines to estimate the score function and simple cross validation to determine the knots of the B-splines, which are a modified version of Kun( 1992). We herein extend this method to right-censored data and study how well the adaptive M-estimator performs for various error distributions and censoring rates. Some impressive simulation results are shown.

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ADAPTIVE CHANDRASEKHAR FILLTER FOR LINEAR DISCRETE-TIME STATIONALY STOCHASTIC SYSTEMS

  • Sugisaka, Masanori
    • 제어로봇시스템학회:학술대회논문집
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    • 1988.10b
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    • pp.1041-1044
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    • 1988
  • This paper considers the design problem of adaptive filters based an the state-space models for linear discrete-time stationary stochastic signal processes. The adaptive state estimator consists of both the predictor and the sequential prediction error estimator. The discrete Chandrasakhar filter developed by author is employed as the predictor and the nonlinear least-squares estimator is used as the sequential prediction error estimator. Two models are presented for calculating the parameter sensitivity functions in the adaptive filter. One is the exact model called the linear innovations model and the other is the simplified model obtained by neglecting the sensitivities of the Chandrasekhar X and Y functions with respect to the unknown parameters in the exact model.

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Analysis of characteristics of position/speed estimator of an adaptive sensorless controller for PMSM (PMSM 적응 센서리스 제어기의 속도/위치 추정기의 특성 분석)

  • Lee, Jin-Woo
    • Proceedings of the KIPE Conference
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    • 2015.07a
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    • pp.503-504
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    • 2015
  • This paper deals with the analysis of characteristics of position and speed estimator of an adaptive sensorless control algorithm for PMSM drives. The analysis shows that the back emf constant variation results in the position estimation error, but does not the speed estimation error. The simulation and experimental results are shown to verify the analysis result and the usefulness of the back emf constant estimator.

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Boundary stress resolution and its application to adaptive finite element analysis

  • Deng, Jianhui;Zheng, Hong;Ge, Xiurun
    • Structural Engineering and Mechanics
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    • v.6 no.1
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    • pp.115-124
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    • 1998
  • A novel boundary stress resolution method is suggested in this paper, which is based upon the displacements of finite element analysis and of high precision with stress boundary condition strictly satisfied. The method is used to modify the Zienkiewicz-Zhu ($Z^2$) a posteriori error estimator and for the h-version adaptive finite element analysis of crack problems. Successful results are obtained.

The adaptive reduced state sequence estimation receiver for multipath fading channels (이동통신 환경에서 적응상태 축약 심볼열 추정 수신기)

  • 이영조;권성락;문태현;강창언
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.22 no.7
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    • pp.1468-1476
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    • 1997
  • In mobile communication systems, the Reduced State Sequence Estimation(RSSE) receiver must be able to track changes in the channel. This is carried out by the adaptive channel estimator. However, when the tentative decisions are used in the channel estimator, incorrect decisions can cause error propagation. This paper presents a new channel estimator using the path history in the Viterbi decoder for preventing error propagation. The selection of the path history in the Viterbi decoder for preventing error propagation. The selection of the path history for the channel estimator depends on the path metric as in the decoding of the Viterbi decoder in RSSE. And a discussion on the channel estimator with different adaptation algorithms such as Least Mean Square(LMS) algorithm and Recursive Least Square(RLS) algorithm is provided. Results from computer simulations show that the RSSE receivers using the proposed channel estimator have better performance than the other conventional RSSE receiver, and that the channel estimator with RLS algorithm is adequate for multipath fading channel.

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