• Journal of the Korea institute for structural maintenance and inspection
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• v.2 no.4
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• pp.194-200
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• 1998

Risk, probability of failure, which includes various uncertainties and influential factors of performance should be accounted for in engineering system. Recently, several different methods to analysis risk evaluation evolved and one of the practical method is FOSM (First Order Second Moment Method ). FOSM method is derived in terms of terms coefficient of variance to uncertainties which influence various factor. For risk evaluation and uncertainty analysis in hydraulic design system, load-capacity relationship is adopted in this paper. Sample catchment with design of sewer system is applied, which plots safety factor vs. risk. Risk evaluation and uncertainty analysis are very to important develop optimal design model in hydraulic system

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.10
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• pp.104-110
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• 2015

The disturbance observer (DOB) controller has been widely used in various industrial applications since it is capable of achieving robust stability and disturbance rejection. In this paper, we study the effect of Q-filter on disturbance observer controller for Electro-Magnetic suspension (EMS) systems. We consider three Q-filters and analyze their effects on the robust stability against parameter uncertainties due to mass variation. Moreover, we investigate the influence of sensor noise for three Q-filters. According to our study, robust stability improves as the order of Q-filter decreases. On the other hand, the larger the order of Q-filter, the more the effect of sensor noise can be removed.

• Journal of the Korea Institute of Military Science and Technology
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• v.19 no.3
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• pp.387-394
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• 2016

This paper proposes a new disturbance observer(DOB). The purpose of the DOB is to realize the plant performing like a model in the presence of disturbances which come from external environment and inherent nonlinearities and uncertainties in the plant. It is shown that the proposed DOB compensates those disturbances, nonlinearities and uncertainties, effectively. And it is theoretically proved that the proposed DOB can be guaranteed its stability for the stable plant. Its availability is shown by applying the DOB to the stabilization platform for EOTS(Electro Optical Tracking System).

• Proceedings of the KSME Conference
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• 2001.06b
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• pp.694-699
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• 2001

This paper presents a control of active suspension system for quarter-car model with two-degree-of-freedom using $H_{inf}$ and nonlinear adaptive robust control method. Suspension dynamics is linear and treated by $H_{inf}$ method which guarantees the robustness of closed loop system under the presence of uncertainties and minimizes the effect of road disturbance to system. An Adaptive Robust Control (ARC) technique is used to design a force controller such that it is robust against actuator uncertainties. Simulation results are given for both frequency and time domains to verify the effectiveness of the designed controllers.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.54.4-54
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• 2001

This paper proposes an adaptive control scheme for an autopilot design of Relaxed-Static-Stability(RSS) Missiles with saturating actuator. The feedback linearization controller eliminates nonlinear terms in RSS missile dynamics and makes the entire system linear. But modeling errors, disturbances and the nonlinear mismatch due to input constraints exert a bad influence on the performance of the feedback linearization controller Thus, first, we derive a parametric affine uncertainty model with modeling errors and disturbances. Then an adaptive control law with anti-windup scheme is developed, where the bounds of uncertainties are estimated with adaptive laws. The proposed adaptive controller can remove the bad effects of uncertainties, of disturbances, and of saturating actuator ...

• Journal of the Korean Geophysical Society
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• v.9 no.3
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• pp.171-179
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• 2006

Predictive equations of ground motions are one of the most important factors in the seismic hazard analysis. Unfortunately, studies on predictive equations of ground motions in Korea had been hampered due to the lack of seismic data. To overcome the lack of data, seismologists adopted the stochastic method based on the seismological model. Korean predictive equations developed by the stochastic method show large differences in their predictions. It was turned out through the analysis of the existing studies that the main sources of the differences are the uncertainties in the (Brune) stress drop and spectral decay rate . Therefore, it is necessary to focus the future research on the reduction of the uncertainties in the two parameters.

• Journal of Sensor Science and Technology
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• v.28 no.4
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• pp.216-220
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• 2019

In this paper, we propose a finite memory filter (estimator) for discrete-time stochastic linear systems with delays in state and measurement. A novel filtering algorithm is designed based on finite memory strategies, to achieve high estimation accuracy and stability under parametric uncertainties. The new finite memory filter uses a set of recent observations with appropriately chosen initial horizon conditions. The key contribution is the derivation of Lyapunov-like equations for finite memory mean and covariance of system state with an arbitrary number of time delays. A numerical example demonstrates that the proposed algorithm is more robust and accurate than the Kalman filter against dynamic model uncertainties.

• Nuclear Engineering and Technology
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• v.51 no.1
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• pp.163-169
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• 2019

The robust controller synthesis and analysis of the water level process in the U-tube system generator (UTSG) is addressed in this paper. The parameter uncertainties of the steam generator (SG) are modeled as multiplicative perturbations which are normalized by designing suitable weighting functions. The relative errors of the nominal SG model with respect to the other operating power level models are employed to specify the weighting functions for normalizing the plant uncertainties. Then, a robust controller is designed based on ${\mu}$-synthesis and D-K iteration, and its stability robustness is verified over the whole range of power operations. A gain-scheduled controller with $H_{\infty}$-synthesis is also designed to compare its robustness with the proposed controller. The stability analysis is accomplished and compared with the previous QFT design. The ${\mu}$-analysis of the system shows that the proposed controller has a favorable stability robustness for the whole range of operating power conditions. The proposed controller response is simulated against the power level deviation in start-up and shutdown stages and compared with the other concerning controllers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.11
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• pp.4595-4610
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• 2020

In the practical communication environment, the accurate channel state information (CSI) is difficult to obtain, which will cause the mismatch of resource and degrade the system performance. In this paper, to account for the channel uncertainties, a robust power allocation scheme for a downlink Non-orthogonal multiple access (NOMA) heterogeneous network (HetNet) is designed to maximize energy efficiency (EE), which can ensure the quality of service (QoS) of users. We conduct the robust optimization model based on worse-case method, in which the channel gains belong to certain ellipsoid sets. To solve the non-convex non-liner optimization, we transform the optimization problem via Dinkelbach method and sequential convex programming, and the power allocation of small cell users (SCUs) is achieved by Lagrange dual approach. Finally, we analysis the convergence performance of proposed scheme. The simulation results demonstrate that the proposed algorithm can improve total EE of SCUs, and has a fast convergence performance.

• Structural Engineering and Mechanics
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• v.78 no.5
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• pp.529-543
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• 2021

Inevitable source-uncertainties in geometry configuration, boundary condition, and material properties may deviate the structural dynamics from its expected responses. This paper aims to examine the influence of these uncertainties on the vibration of functionally graded beams. Finite element procedures are presented for Timoshenko beams and utilized to generate reliable datasets. A prerequisite to the uncertainty quantification of the beam vibration using Monte Carlo simulation is generating large datasets, that require executing the numerical procedure many times leading to high computational cost. Utilizing artificial neural networks to model beam vibration can be a good approach. Initially, the optimal network for each beam configuration can be determined based on numerical performance and probabilistic criteria. Instead of executing thousands of times of the finite element procedure in stochastic analysis, these optimal networks serve as good alternatives to which the convergence of the Monte Carlo simulation, and the sensitivity and probabilistic vibration characteristics of each beam exposed to randomness are investigated. The simple procedure presented here is efficient to quantify the uncertainty of different stochastic behaviors of composite structures.