• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.3
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• pp.363-368
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• 2004

In this paper, the robustness property of 2nd-order iterative learning control(ILC) method for a class of linear and nonlinear discrete-time dynamic systems is studied. 2nd-order ILC method has the PD-type learning algorithm based on both time-domain performance and iteration-domain performance. It is proved that the 2nd-order ILC method has robustness in the presence of state disturbances, measurement noise and initial state error. In the absence of state disturbances, measurement noise and initialization error, the convergence of the 2nd-order ILC algorithm is guaranteed. A numerical example is given to show the robustness and convergence property according to the learning parameters.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.171-171
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• 2020

Decision making strategies should consider both adaptiveness and robustness in order to deal with two main characteristics of climate change: non-stationarity and deep uncertainty. Especially, robust strategies are different from traditional optimal strategies in the sense that they are satisfactory over a wider range of uncertainty and may act as a key when confronting climate change. In this study, a new framework named Robust Stochastic Dynamic Programming (R-SDP) is proposed, which couples previously developed robust optimization (RO) into the objective function and constraint of SDP. Two main approaches of RO, feasibility robustness and solution robustness, are considered in the optimization algorithm and consequently, three models to be tested are developed: conventional-SDP (CSDP), R-SDP-Feasibility (RSDP-F), and R-SDP-Solution (RSDP-S). The developed models were used to derive optimal monthly release rules in a single reservoir, and multiple simulations of the derived monthly policy under inflow scenarios with varying mean and standard deviations are undergone. Simulation results were then evaluated with a wide range of evaluation metrics from reliability, resiliency, vulnerability to additional robustness measures. Evaluation results were finally visualized with advanced visualization tools that are used in multi-objective robust decision making (MORDM) framework. As a result, RSDP-F and RSDP-S models yielded more risk averse, or conservative, results than the CSDP model, and a trade-off relationship between traditional and robustness metrics was discovered.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.420-423
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• 1995

The stability of system is one of the important aspects and to judge system's stability is another complicated problem. Previously, new technique derived from relaxing Lyapunov conditions has been already introduced and in this paper, this proposed technique applies to the practical dynamic systems. This utility of numerical procedures prove the comparable improvements of the estimation of robustness for dynamic systems having structured (bounded) perturbations.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.1 s.190
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• pp.14-19
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• 2007

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.4
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• pp.392-399
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• 1992

This paper will describe the application of variable structure control theory to the first order dynamic system and verify its robustness. The study on the first order dynamic system control which has been essential part for the control of servo motor (AC,DC) systems has been excluded in the study of variable structure control system(VSCS) because this first order system was not applicable to the previous variable structure control theory. So, for the robustness control of first order dynamic system with variable structure control theory, we propose modified switching function synthesis which guarantees the advantages of conventional VSCS and removes reaching phase which is regarded as shortcomings in VSCS. And we demonstrate the practical potential of implementation about this theory by simulation results of AC motor variable speed control.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.179-184
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• 1991

This paper will describe the application for variable structure control theory to the first order dynamic system and verify it's robustness. The study on the first order dynamic system control which has been essential part for the control of servo motor (AC, DC) systems has been excluded in the study of variable structure control system(VSCS) because this first order system was not applicable to the previous variable structure control theory. So, for the robustness control of first order dynamic system with variable structure control theory, we propose modified switching function synthesis which guarantees the advantages of conventional VSCS and removes reaching phase which regards as shortcomings in VSCS. And we demonstrate the practical potential of implementation about this theory by simulation results of AC motor variable speed control.

• Journal of Distribution Science
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• v.22 no.9
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• pp.65-72
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• 2024

Purpose: This study aims to empirically analyze the influence of supply chain agility and flexibility on supply chain robustness and logistics performance, addressing a research gap in the context of dynamic business environments. Research design, data and methodology: The study examines causal relationships between supply chain agility, flexibility, robustness, and logistics performance among businesses in South Korea. Data were collected through a survey of 300 workers in supply chain-related departments. A structural equation model was employed for hypothesis testing. Results: The empirical analysis shows that supply chain agility and flexibility positively and significantly influence supply chain robustness, which in turn has a significant positive impact on logistics performance. Conclusions: This study contributes by providing empirical evidence on the importance of supply chain agility, flexibility, and robustness in enhancing logistics performance. The findings suggest prioritizing the development of these capabilities for competitive advantage. Further research on the interrelationships between various supply chain capabilities and their impact on performance outcomes is highlighted.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1816-1819
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• 1997

Using the vision system, robotic tasks in unstructured environments can be accompished, which reduces greatly the cost and steup time for the robotic system to fit to he well-defined and structured working environments. This paper proposes a dynamic control scheme for robot manipulator with eye-in-hand camera configuration. To perfom the tasks defined in the image plane, the camera motion Jacobian (image Jacobian) matrix is used to transform the camera motion to the objection position change. In addition, the dynamic learning controller is designed to improve the tracking performance of robotic system. the proposed control scheme is implemented for tasks of tracking moving objects and shown to outperform the conventional visual servo system in convergence and robustness to parameter uncertainty, disturbances, low sampling rate, etc.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.525-530
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• 2003

This paper is concerned with a control allocation strategy using the dynamic inversion which generates the nominal control input trajectories, and autopilot design using the time-varying control technique which is time-varying version of pole placement of linear time-invariant system for an agile missile with aerodynamic fin and thrust vectoring control. Dynamic inversion can decide the amount of the deflection of each control effector, aerodynamic fin and thrust vectoring control, to extract the maximum performance by combining the action of them. Time-varying control technique for autopilot design enhance the robustness of the tracking performance for a reference command. Nonlinear simulations demonstrates the dynamic inversion provides the effective nominal control input trajectories to achieve the angle of attack command, and time-varying control technique exhibits good robustness for a wide range of angle of attack.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.2
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• pp.109-118
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• 2008

We present a robust localization algorithm using particle filter for mobile robots in a dynamic environment. It is difficult to describe moving obstacles like people or other robots on the map and the environment is changed after mapping. A mobile robot cannot estimate its pose robustly with this incomplete map because sensor observations are corrupted by un-modeled obstacles. The proposed algorithms provide robustness in such a dynamic environment by suppressing the effect of corrupted sensor observations with a selective update or a sampling from non-corrupted window. A selective update method makes some particles keep track of the robot, not affected by the corrupted observation. In a sampling from non-corrupted window method, particles are always sampled from several particle sets which use only non-corrupted observation. The robustness of proposed algorithm is validated with experiments and simulations.