• East Asian Economic Review
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• v.28 no.1
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• pp.95-140
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• 2024

This study empirically investigates the international transmission of China's uncertainty shocks. It estimates a time-varying parameter Bayesian global structural vector autoregressive model (TVP-BGVAR) using time series data for 33 countries to evaluate heterogeneous international linkage across countries and time. Uncertainty shocks are identified via sign restrictions. The empirical results reveal that an increase in uncertainty in China negatively affects the global economy, but those effects significantly vary over time. The effects of China's uncertainty shocks on the global economy have been significantly altered by China's WTO accession, the global financial crisis, and the recent US-China trade conflict. Furthermore, the effects of China's uncertainty shocks, typically on inflation, differ significantly across countries. Moreover, Trade openness appears crucial in explaining heterogeneous GDP responses across countries, whereas the international dimension of monetary policy appears to be important in explaining heterogeneous inflation responses across countries.

• Journal of the Korean Data and Information Science Society
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• v.27 no.1
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• pp.9-18
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• 2016

In the Heckman-Meyers model, which is frequently referred by IAA, Swiss Solvency Test, EU Solvency II, the assumption of parameter distribution is key factor. While in theory Bayesian analysis somewhat reflects parameter uncertainty using prior distribution, it is often the case where both Heckman-Meyers and Bayesian are necessary to better manage the parameter uncertainty. Therefore, this paper proposes the use of Bayesian H-M CRM, a combination of Heckman-Meyers model and Bayesian, and analyzes its efficiency.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.8
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• pp.923-931
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• 1999

In this paper, a modeling and a robust time-delay control for the reclaimer are investigated. Supplying the same amount of a raw material throughout the reclamation process from the raw yard to a sinter plant is important to keep the quality of the molten steel uniform in blast furnaces. As the actual parameter values of the reclaimer are not available, the boom rotational dynamics are modeled as a second order differential equation with unknown coefficients. The unknown parameters in the nominal model are estimated using a recursive estimation method. Another important factor in the control design of the reclaimer is the large time-delay in output measurement. Assuming a multiplicative uncertainty, that accounts for both the unstructured uncertainty neglected in the modeling and the structured uncertainty contained in the parameter estimation, a robust Smith predictor is designed. A robust stability criterion for the multiplicative uncertainty is also derived. Following the work of Goodwin et al. [4], a quantifying procedure of the multiplicative uncertainty bound, through experiments , is described. Experimental and simulation results are provided.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.3
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• pp.175-181
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• 2000

This paper investigates the robust nonlinear H$_{\infty}$ filter with FIR(Finite Impulse Response) structure for nonlinear discrete time-varying uncertain systems represented by the state-space model having parameter uncertainty. Firstly, when there is no parameter uncertainty in the system, the discrete-time nominal nonlinear H$_{\infty}$ FIR filter is derived by using the equivalence relationship between the FIR filter and the recursive filter, which corresponds to the standard nonlinear H$_{\infty}$ filter. Secondly, when the system has the parameter uncertainty, the robust nonlinear H$_{\infty}$ FIR filter is proposed for the discrete-time nonlinear uncertain systems.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.75.3-75
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• 2001

This paper addresses the design of State-feedback Robust Guaranteed-Cost Controllers with controller gain variations. Since the unstructured uncertainty is the most dominant uncertainty in the modeling of the plant, the plant is assumed to have the unstructured uncertainty. It is necessary to take the controller parameter perturbation into consideration when we design the robust controller. Otherwise, the resulting controller may show the fragility property. That is to say, the extremely small controller parameter variation may result in the instability of the overall closed-loop system. Therefore, the design purpose is that the maximum performance index is guaranteed in the presence of the unstructured plant uncertainty and controller parameter variations ...

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.7
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• pp.919-926
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• 2012

In order to control a LTI(Linear Time Invariant) system subjected to system noise and measurement noise, first of all, it is necessary to estimate the state of system with reliability. Kalman filtering technique has been widely used to estimate the state of the stochastic LTI system with stationary noise characteristics because of its estimation ability versus algorithm simplicity. However, it often fails to estimate the state of the LTI system of which system parameter uncertainty exists partly and/or input uncertainty exists. In this paper, a new estimation technique based on Kalman filter is suggested for stochastic LTI system under parameter uncertainty and/or input uncertainty. A fuzzy estimation algorithm against uncertainties is introduced so as to compensate the state estimate filtered by Kalman filter. In order to verify the state estimation performance of the suggested technique, several simulations are accomplished.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.355-359
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• 1993

This paper deals with the problem of robust TDF(Two Degree of Freedom) H$_{\infty}$ control design for a linear system with parameter uncertainty in the state space model. The uncertain system considered here is with the time-invariant norm-bounded parameter uncertainty in the state matrix. A TDF H$_{\infty}$ control design is presented which robustly stabilizes the plant, guarantees the robust H$_{\infty}$ performance and improves the tracking performance for the closed-loop system in the face of parameter uncertainty. It is shwon that a suitable stabilizing control law can be constructed in terms of a positive definite solution to a certain parameter-dependent algebraic Riccati equation and a good tracking performance can be constructed in terms of suitable feedforward control law.aw.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.3
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• pp.270-277
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• 1999

This paper deals with the maneuver detection and target tracking problem in uncertain parameter systems using a robust{{{{ { H}_{ } }}}} FIR filter to improve the unacceptable tracking performance due to the parametr uncertainty. The tracking filter used in the current paper is based on the robust{{{{ { H}_{ } }}}} FIR filter proposed by Kwon et al. [1,2] to estimate the state signal in uncertain systems with parameter uncertainty, and the basic scheme of the proposed method is the input estimation approach. Tracking performance of the maneuver detection and target tracking method proposed is compared with other techniques, Bogler allgorithm [4] and FIR tracking filter [2], via some simulations to examplify the good tracking performance of the proposed method over other techniques.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.438-443
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• 2003

In this paper, we propose a control algorithm for two-wheel mobile robot that can move the rider to his or her command and autonomously keep its balance. The control algorithm is based on a mixed $H_2/H_{\infty}$ control scheme. In this control problem the main issue is to move the rider while keeping its balance in the presence of disturbances and parameter uncertainties. The disturbance force caused by uneven road surfaces and the uncertainty due to different rider's heights are considered. To this end we first consider a state feedback controller as a basic framework. Secondly, we obtain the state feedback gain $K_2$ minimizing the $H_2$ norm and the state feedback gain $K_{\infty}$ minimizing the $H_{\infty}$ norm over the whole range of parameter uncertainty. Finally, we select mixed $H_2$/$H_{\infty}$ state feedback controller K as the geometric mean of $K_2$ and $K_{\infty}$. Simulation results show that the mixed $H_2/H_{\infty}$ state feedback controller combines the effects of the optimal $H_2$ state feedback controller and robust $H_{\infty}$ controller state feedback controller efficiently in the presence of disturbance and parameter uncertainty.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.723-726
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• 2008

It is not always easy to estimate the parameters in hydrologic models due to insufficient hydrologic data when hydraulic structures are designed or water resources plan are established, uncertainty analysis, therefore, are inevitably needed to examine reliability for the estimated results. With regard to this point, this study applies a Bayesian Markov Chain Monte Carlo scheme to the NWS-PC rainfall-runoff model that has been widely used, and a case study is performed in Soyang Dam watershed in Korea. The NWS-PC model is calibrated against observed daily runoff, and thirteen parameters in the model are optimized as well as posterior distributions associated with each parameter are derived. The Bayesian Markov Chain Monte Carlo shows a improved result in terms of statistical performance measures and graphical examination. The patterns of runoff can be influenced by various factors and the Bayesian approaches are capable of translating the uncertainties into parameter uncertainties. One could provide against an expected runoff event by utilizing information driven by Bayesian methods. Therefore, the rainfall-runoff analysis coupled with the uncertainty analysis can give us an insight in evaluating flood risk and dam size in a reasonable way.