• Communications for Statistical Applications and Methods
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• v.10 no.1
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• pp.101-113
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• 2003

Quasi likelihood estimators defined by Wedderburn are derived for several nonlinear time series models. And also, the least squared estimator and Quasi-likelihood estimator are compared in sense of asymptotic relative efficiency at those models. Finally, we apply these estimations to a real data on exchanging rate and stock market prices.

• Communications for Statistical Applications and Methods
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• v.8 no.3
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• pp.633-641
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• 2001

The least squares method is usually applied when estimating the parameters in the regression models. However the least square estimator is not very efficient when the distribution of the error is skewed. In this paper, we propose the asymmetric least square estimator for a particular nonlinear time series regression model, and give the simple and practical sufficient conditions for the strong consistency of the estimators.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2001.04a
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• pp.180-189
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• 2001

In this study, recently developed unscented Kalman filter (UKF) technique is studied for identification of nonlinear structural dynamic systems as an alternative to the extended Kalman filter (EKF). The EKF, which was originally developed as a state estimator for nonlinear systems, has been frequently employed for parameter identification by introducing the state vector augmented with the unknown parameters to be identified. However, the EKF has several drawbacks such as biased estimations and erroneous estimations especially for highly nonlinear dynamic systems due to its crude linearization scheme. To overcome the weak points of the EKF, the UKF was recently developed as a state estimator. Numerical simulation studies have been carried out on nonlinear SDOF system and nonlinear MDOF system. The results from a series of numerical simulations indicate that the UKF is superior to the EKF in the system identification of nonlinear dynamic systems especially highly nonlinear systems.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2001.10a
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• pp.117-126
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• 2001

In this study, the recently developed unscented Kalman filter (UKF) technique is studied for identification of nonlinear structural dynamic systems as an alternative to the extended Kalman filter (EKF). The EKF, which was originally developed as a state estimator for nonlinear systems, has been frequently employed for parameter identification by introducing the state vector augmented with the unknown parameters to be identified. However, the EKF has several drawbacks such as biased estimations and erroneous estimations especially for highly nonlinear dynamic systems due to its crude linearization scheme. To overcome the weak points of the EKF, the UKF was recently developed as a state estimator. Numerical simulation studies have been carried out on nonlinear SDOF system and nonlinear MDOF system. The results from a series of numerical simulations indicate that the UKF is superior to the EKF in the system identification of nonlinear dynamic systems especially highly nonlinear systems.

• Transactions of the Korean Society of Automotive Engineers
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• v.9 no.2
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• pp.176-184
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• 2001

The VDC(Vehicle Dynamics Control) is a control system whose target is to improve vehicle stability under critical motion. The system has a good potential of becoming a standard active safety unit in passenger vehicles since it can be implemented on top of the ABS/TCS system with little extra cost. This, however, is possible only when the signals that the VDC system demands can be obtained with sufficient accuracy. In this research, estimators for the road friction coefficient and body sideslip angle have been designed. The two variables have great influence upon performance of the VDC system but not directly measurable. For the estimator design, the Newton method and the nonlinear observer theory have been exploited. The performance of the estimator have been verified via simulations on critical driving conditions.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.12
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• pp.1865-1874
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• 2013

In this paper, a design method of speed and position estimator based on unscented Kalman filter is proposed for the no sensor control of IPMSM(Interior Permanent Magnet Synchronous Motor). The proposed method is simple more than the estimator designed with rotation axis for current measurement. Also the proposed state estimator is designed including nonlinear terms of the estimator. The controller which constructed using nonlinear back-stepping control method is operated speed and current control using the estimated speed and currents information. Through simulation, the performance of the designed estimator is compared to the estimator which is designed to synchronize d-q axis.

• Journal of IKEEE
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• v.9 no.1 s.16
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• pp.72-78
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• 2005

This paper describes the design of a robust output-feedback controller for a single-input single-output nonlinear dynamical system with a full relative degree. While all the previous research works on the output-feedback control are based on dynamic observers, a new state estimator which uses the past values of the measurable system output is proposed. We name it backward-difference state estimator since the derivatives of the output are estimated simply by backward difference of the present and past values of the output. The disturbance generated due to the error between the estimated and real state variables is compensated using an additional robustifying control law whose gain is tuned adaptively. Overall control system guarantees that the tracking error is asymptotically convergent and that all signals involved are uniformly bounded. Theoretical results are illustrated through a simulation example of inverted pendulum.

• Wind and Structures
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• v.17 no.4
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• pp.451-464
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• 2013

This study develops an extended inverse input estimation algorithm with intelligent adaptive fuzzy weighting to effectively estimate the unknown input wind load of nonlinear structural systems. This algorithm combines the extended Kalman filter and recursive least squares estimator with intelligent adaptive fuzzy weighting. This study investigated the unknown input wind load applied on a tower structural system. Nonlinear characteristics will exist in various structural systems. The nonlinear characteristics are particularly more obvious when applying larger input wind load. Numerical simulation cases involving different input wind load types are studied in this paper. The simulation results verify the nonlinear characteristics of the structural system. This algorithm is effective in estimating unknown input wind loads.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.55 no.9
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• pp.429-434
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• 2006

In this paper, detection based - adaptive windowed nonlinear filter(DB-AWNF) is proposed for removing salt-pepper noise in infrared image. This filter is composed of impulse detector and window-size-variable median filters. Impulse detector checks whether current pixel is impulse or not using range function and nonlinear location estimator. If impulse is detected, current pixel is filtered according to four kinds of local masks by use of median filter. If not, current pixel is delivered to output like identity filter. In Qualitative view, the proposed could have removed heavy corrupted noise up to 30% and reserved the details of image. In quantitative view, PSNR was measured. The proposed could have about 12-31[dB] more improved performance than those of median $(3{\times}3)$ filter and 13-29[dB] more improved performance than those of median $(5{\times}5)$ filter.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.7
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• pp.428-436
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• 2005

In this paper, we introduce an adaptive control method to improve the position accuracy and reduce nonlinear friction effects for the linear motion servo system with the nonlinear friction. The considered system plant included not only the variation of the mass of mover but also the friction change by the normal force. We also designed an adaptive controller with the mass estimator and the compensator by observing the variation of normal force. The effectiveness and system performances for the proposed control method in this paper show to improve than other control methods through numerical simulations.