• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.48.2-48
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• 2002

This paper presents an integrated sliding mode adaptive control scheme for general nonlinear uncertain systems, where structured uncertainty is assumed can be linearly parameterized and unstructured uncertainty is assumed be bounded by unknown constant A certain estimation scheme for this unknown constant is introduced to attenuate the unstructured uncertainty. Presented control scheme is shown to be stable and numerical expressions of bounds of all error signals are given, from which we can acquire some useful information about practical trade-off between tracking performance and parameter estimation property.

• Proceedings of the KIEE Conference
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• 2006.04a
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• pp.201-203
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• 2006

This paper describes a new LOS(Line-of-Sight) estimator for BTT missiles. The dynamic models of LOS rate and a seeker are derived. Based on these dynamic models, we design a nonlinear estimator, which takes into account roll motion of BTT missiles and sensor noises. Simulation results show that the LOS rate estimates of the proposed estimator are more accurate than those of the conventional estimator.

• Proceedings of the Korea Society for Simulation Conference
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• 1992.10a
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• pp.6-6
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• 1992

This work deals with nonlinear least squares method for estimating unknown universial constants C in a computer simulation code real experimental data(or database) and computer simulation data. The best linear unbiased predictor based on a spatial statistical model is fitted from the computer simulation data. Then nonlinear least squares estimation method is applied to the real data using the fitted prediction model(or simulation predictor) as if it were the true simulation model. An application to the computational nuclear fusion device is presented.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.05a
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• pp.506-509
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• 2006

Extended Kalman Filter iterate the prediction and the filtering based on Initial state for the next time step. EKF method for the estimation of nonlinear parameters of a structural dynamic system is necessary that initial of state vector and error covariance matrix. Because those are unknown exactly, generally selected random values. That occasion observability problem appear because of unknown initial values. In this study, for the estimation of the nonlinear parameters, a simple one degree of Freedom example is carried out by Extended Kalman Filter. And initial value assumption for Parameter Estimation of Dynamic System are developed. The result of analysis is compared with calculated standard values.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.9
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• pp.865-872
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• 2008

Spacecraft attitude estimation using the nonlinear unscented filter is addressed to fully utilize capabilities of the unscented transformation. To release significant computational load, an efficient technique is proposed by reasonably removing correlation between random variables. This modification introduces considerable reduction of sigma points and computational burden in matrix square-root calculation for most nonlinear systems. Unscented filter technique makes use of a set of sample points to predict mean and covariance. The general QUEST(QUaternion ESTimator) algorithm preserves explicitly the quaternion normalization, whereas extended Kalman filter(EKF) implicitly obeys the constraint. For spacecraft attitude estimation based on quaternion, an approach to computing quaternion means from sampled quaternions with guarantee of the quaternion norm constraint is introduced applying a constrained optimization technique. Finally, the performance of the new approach is demonstrated using a star tracker and rate-gyro measurements.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.45 no.3
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• pp.399-406
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• 1996

This paper presents a method of hierarchical state estimation of the time-varying linear systems via Block-pulse function(BPF). When we estimate the state of the systems where noise is considered, it is very difficult to obtain the solutions because minimum error variance matrix having a form of matrix nonlinear differential equations is included in the filter gain calculation. Therefore, hierarchical approach is adapted to transpose matrix nonlinear differential equations to a sum of low order state space equation from and Block-pulse functions are used for solving each low order state space equation in the form of simple and recursive algebraic equation. We believe that presented methods are very attractive nd proper for state estimation of time-varying linear systems on account of its simplicity and computational convenience. (author). 13 refs., 10 figs.

• Transactions of the Korean Society of Automotive Engineers
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• v.11 no.4
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• pp.158-164
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• 2003

The suspension system of cars is composed of dampers and springs, which usually have nonlinear characteristics. The nonlinear characteristics make the differences in the results of analytical models and experiments. In this study, the nonlinear system identification method which does not assume a special form for nonlinear dynamic systems and minimize the error by calculating the error reduction ratio is devised to estimate the nonlinear parameters of the suspension system of an EF-SONATA car from the field running test data. The results show that the spring has a cubic nonlinear term and the damper has a coupled nonlinear term. Also, the numerical results with the estimated nonlinear parameters agree well with the field test data for the different running speeds.

• Journal of the Korean Statistical Society
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• v.30 no.3
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• pp.387-406
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• 2001

Adaptive estimators for a class of nonlinear time series models has been proposed by several authors. Koul and Schick(1997) proposed the adaptive estimators without sample splitting for location-type time series models. They also showed by simulation that the adaptive estimators without sample splitting have smaller mean squared errors than those of the adaptive estimators with sample splitting. the present paper generalized the result in a case of location-scale type nonlinear time series models by simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.560-563
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• 1997

The conventional impedance control has been known to have the following problems: it has lack of specifying force directly and unknown environment stiffness has to be known priori in order to specify the reference trajectory. In this paper, new impedance force control that can control a desired force directly under unknown stiffness is proposed. A new nonlinear impedance function is developed based on estimation of unknown stiffness from force and position measurements. The nonlinear characteristics of the proposed impedance function are analyzed based on unknown environment position. Simulation studies with robot manipulator are carried out to test analytical results.

• Communications of the Korean Mathematical Society
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• v.11 no.4
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• pp.1147-1157
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• 1996

Nonlinear ill-posed problems arise in many application including parameter estimation and inverse scattering. We introduce a least squares regularization method to solve nonlinear ill-posed problems with constraints robustly and efficiently. The regularization method uses Trust-Region approach to handle the constraints on variables. The Generalized Cross Validation is used to choose the regularization parameter in computational tests. Numerical results are given to exhibit faster convergence of the method over other methods.