• Structural Monitoring and Maintenance
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• v.9 no.2
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• pp.107-127
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• 2022

Structural health monitoring (SHM) has been related to damage identification with either operational loads or other environmental loading playing a significant complimentary role in terms of structural safety. In this study, a non-parametric method of time frequency analysis on the measurement is used to address the time-frequency representation for modal parameter estimation and system damage identification of structure. The method employs the wavelet decomposition of dynamic data by using the modified complex Morlet wavelet with variable central frequency (MCMW+VCF). Through detail discussion on the selection of model parameter in wavelet analysis, the method is applied to study the dynamic response of both steel structure and reinforced concrete frame under white noise excitation as well as earthquake excitation from shaking table test. Application of the method to building earthquake response measurement is also examined. It is shown that by using the spectrogram generated from MCMW+VCF method, with suitable selected model parameter, one can clearly identify the time-varying modal frequency of the reinforced concrete structure under earthquake excitation. Discussions on the advantages and disadvantages of the method through field experiments are also presented.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.6
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• pp.20-26
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• 2002

This paper deals with the guaranteed cost control problems for a class of discrete-time linear uncertain systems with time-varying delay. The uncertain systems under consideration depend on time-varying norm-bounded parameter uncertainties. We address the existence condition and the design method of the memoryless state feedback control law such that the closed loop system not only is quadratically stable but also guarantees an adequate level of performance for all admissible uncertainties. Through some changes of variables and Schur complement, It is shown that the sufficient condition can be rewritten as an LMI(linear matrix inequality) form in terms of all variables.

• Journal of Korea Water Resources Association
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• v.54 no.6
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• pp.365-379
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• 2021

Hydrological model parameters are essential for model simulation and can vary over time due to topography, climatic conditions, climate change and human activity. Consequently, the use of fixed parameters can lead to inaccurate stream flow simulations. The aim of this study is to investigate an appropriate method of estimating time-varying parameters using stream flow observations, and how the simulation efficiency changes when stream flow data are assimilated into the model. The data assimilation method can be used to automatically estimate the parameters of a hydrological model by adapting to a variety of changing environments. Stream flow observations were assimilated into a two parameter monthly water balance model using a particle filter. The simulation results using the time-varying parameters by the data assimilation method were compared with the simulation results using the fixed parameters by the SCEM method. First, we conducted synthesis experiments based on various scenarios to investigate if the particle filter method can adequately track parameters that change over time. After that, it was applied to actual watersheds and compared with the predictive performance of stream flow when using parameters that change with time and fixed parameters. The conclusions obtained through this study are as follows: (1) The predictive performance of the overall monthly stream flow time series was similar between the particle filter method and the SCEM method. (2) The monthly runoff prediction performance in the period except the rainy season was better in the simulation by the periodically changing parameters using the data assimilation method. (3) Uncertainty in the observational data of stream flow used for assimilation played an important role in the predictive performance of the particle filter.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.138-141
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• 2003

Recently reinforcement learning has attracted attention of many researchers because of its simple and flexible learning ability for any environments. And so far many reinforcement learning methods have been proposed such as Q-learning, actor-critic, stochastic gradient ascent method and so on. The reinforcement learning system is able to adapt to changes of the environment because of the mutual action with it. However when the environment changes periodically, it is not able to adapt to its change well. In this paper we propose the reinforcement learning system that is able to adapt to periodical changes of the environment by introducing the time-varying parameters to be adjusted. It is shown that the proposed method works well through the simulation study of the maze problem with aisle that opens and closes periodically, although the conventional method with constant parameters to be adjusted does not works well in such environment.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.41 no.9
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• pp.1141-1145
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• 2016

In general, a variable forgetting factor is applied to the RLS algorithm for the time-varying parameter estimation in the non-stationary environments. The introduction of a variable forgetting factor to RLS needs heavy additional calculation complexity. We propose a new Gauss Newton variable forgetting factor RLS algorithm which needs small amount of calculation as well as estimates the better parameters in time-varying nonstationary environment. The algorithm performs as good as the conventional Gauss Newton variable forgetting factor RLS and the required additional calculation complexity reduces from $O(N^2)$ to O(N).

• Journal of Power Electronics
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• v.18 no.1
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• pp.147-159
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• 2018

In this paper, the voltage tracking control of a conventional DC-DC boost converter affected by unknown, time-varying circuit parameter perturbations is investigated. Based on the fundamental property of incremental passivity, a passivity based control law is designed. Then, to obtain a better disturbance rejection property, two generalized proportional integral (GPI) observers are employed to estimate the time-varying uncertainties in the output voltage and inductor current channels, and the estimated values are applied as feedforward compensation. Moreover, the global trajectory tracking performance of a system with disturbances is ensured under the composite controller. Finally, simulation and experiment studies are provided to demonstrate the feasibility and effectiveness of the proposed method. The results show that the proposed controller delivers a promising disturbance rejection capability as well as a good nominal tracking performance.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.386-391
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• 2003

This paper focuses on robust $H_{\infty}$ control for bilinear systems with time-varying parameter uncertainties and exogenous disturbance via output feedback. $H_{\infty}$ control is achieved via separation into a $H_{\infty}$ state feedback control problem and a $H_{\infty}$ state estimation problem. The suitable robust stabilizing output feedback control law can be constructed in term of approximated solution to x-dependent Riccati equation using successive approximation technique. Also, the $H_{\infty}$ filter gain can be constructed in term of solution to algebraic Riccati equation. The output feedback control robustly stabilizes the plant and guarantees a robust $H_{\infty}$ performance for the closed-loop systems in the face of parameter uncertainties and exogenous disturbance.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1037-1041
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• 1996

Input-output linearization technique in nonlinear control does not guarantee the robustness in the presence of parameter uncertainty or unmodeled dynamics, etc. However, it has been used as an important preliminary step in achieving additional control objectives, for instance, robustness to parameter uncertainty and disturbance attenuation. An indirect adaptive control scheme based on input-output linearization is proposed in this paper. The scheme consists of a Hopfield network for process parameter identification and an adaptive sliding mode controller based on input-output linearization, which steers the system response into a desired configuration. A numerical example is presented for the trajectory tracking of uncertain nonlinear dynamic systems with slowly time-varying parameters.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.514-518
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• 2003

In this paper, we investigate three dimensional optimal evasive maneuver patterns for air-to-surface attack missiles against proportionally navigated anti-air defense missiles. Interception error of the defense missile can be generated by evasive maneuver of the attack missile during the time of flight for which the defense missile intercepts the attack missile. Time varying weighted sum of the inverse of these interception errors forms a performance index to be minimized. Direct parameter optimization technique using CFSQP is adopted to get the attack missile's optimal evasive maneuver patterns according to parameter changes of both the attack missile and the defense missile such as maneuver limit and time constant of autopilot approximated by the 1st order lag system. The overall shape of resultant optimal evasive maneuver to enhance the survivability of air-to-surface missiles against proportionally navigated anti-air missiles is a kind of deformed barrel roll.

• Journal of the Korean Institute of Intelligent Systems
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• v.8 no.5
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• pp.21-28
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• 1998

This study develops a variable structure controller using the time-varying nonlinear sliding surface instead of the fixed sliding surface, which has been the robustness against parameter variations and extraneous disturbance during the reaching phase. By appling TS fuzzy algorithm to the regulation of the rionlinear sliding surface, the reaching time of the system trajectory is faster than the fixed method. This proposed scheme has better performance than the conventional method in reaching time, parameter variation and extraneous disturbance. To demonstrate its performance, the proposed control algorithm is applied to a rotational inverted pendulum.