• Journal of Soil and Groundwater Environment
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• v.25 no.1
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• pp.37-52
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• 2020

Sensitivity analysis of hydrodynamic and reaction parameters in conceptual model reflecting aquifer characteristics of Korea was performed to evaluate the uncertainty in the predicted concentrations. Among the hydrodynamic input parameters, both hydraulic conductivity (K_x) and hydraulic gradient (I) affected transport behaviors of contaminants, and resulted in same convergence concentrations with continuous injections of contaminant. However, longitudinal dispervisity (α_L) affected both transport behaviors and the convergence concentrations of contaminants. Compared to the hydrodynamic parameters, growth kinetic and degradation parameters (μ_m & K_c) more significantly affected both transport behaviors and the convergence concentrations of contaminants, indicating those parameters had higher sensitivity indices causing the uncertainties of model predictions. Considering that the sensitivity indices of both hydrodynamic and reaction parameters were a function of transport distance of groundwater, the parameters with higher sensitivity indices, a priori, need to be investigated using conceptual model reflecting site-specific aquifer characteristics before field investigation. After determining the parameters with higher sensitivity indices, the detail field investigations for the selected hydrodynamic and reaction parameters were warranted to reduce the uncertainties of model predictions.

• Journal of Korea Water Resources Association
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• v.47 no.2
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• pp.135-143
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• 2014

In this study, Chungju inflow was simulated for climate change considering the uncertainties of GCMs and a stochastic model. TFN (Transfer Function Noise) model and 4 different GCMs (CNRM, CSIRO, CONS, UKMO) based on IPCC AR4 A2 scenario were used. In order to evaluate uncertainty of TFN model, 100 cases of noises are applied to the TFN model. Thus, 400 cases of inflow results are simulated. Future inflows according to the GCMs show different rates of changes for the future 3 periods relative to the past 30-years reference period. As the results, the summer inflow shows increasing trend and the spring inflow shows decreasing trend based on AR4 A2 scenario.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.2033-2036
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• 2004

A novel approach to estimate the real time moving trajectory of an object is proposed in this paper. The object position is obtained from the image data of a CCD camera, while a state estimator predicts the linear and angular velocities of the moving object. To overcome the uncertainties and noises residing in the input data, a Kalman filter and neural networks are utilized. Since the Kalman filter needs to approximate a non-linear system into a linear model to estimate the states, there always exist errors as well as uncertainties again. To resolve this problem, the neural networks are adopted in this approach, which have high adaptability with the memory of the input-output relationship. Kohonen Network(Self-Organized Map) is selected to learn the motion trajectory since it is spatially oriented. The superiority of the proposed algorithm is demonstrated through the real experiments.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1249-1253
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• 2004

A robust input modification approach to the control of flexible joint robot is presented. In our previous study, we developed an observer based state feedback control for the suppression of residual vibration of a robot. The control was very effective in suppressing the inherent vibration of a flexible joint robot. However it did not meet high performance requirements under high speed motion and model uncertainties. As a solution of the problem, we present an input modification method with robustness against parametric uncertainties. The main idea of the proposed input modification method is to generate a modified reference position command for fast and accurate motion of the robot. Using this proposed method we can reduce the servo delay and settling time by about 60% and substantially improve the path accuracy.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.658-662
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• 2004

This paper develops a stability analysis and controller synthesis methodology for a continuous-time affine Takagi-Sugeno (T-S) fuzzy systems with parametric uncertainties. Affine T-S fuzzy system can be an advantage because it may be able to approximate nonlinear functions to high accuracy with fewer rules than the homogeneous T-S fuzzy systems with linear consequents only. The analysis is based on Lyapunov functions that are continuous and piecewise quadratic. The search for a piecewise quadratic Lyapunov function can be represented in terms of bilinear matrix inequalities (BMIs). A simulation example is given to illustrate the application of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.565-569
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• 2003

In this paper, a robust filter is proposed to effectively estimate the system states in the case where system model uncertainties as well as disturbances are present. The proposed robust filter is constructed based on the linear approximation methods for a general nonlinear uncertain system with an integral quadratic constraint. We also derive the important characteristic of the proposed filter, a modified $H_{\infty}$ performance index. Analysis results show that the proposed filter has robustness against disturbances, such as process and measurement noises, and against parameter uncertainties. Simulation results show that the proposed filter effectively improves the performance.

• Proceedings of the KIEE Conference
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• 1996.07a
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• pp.364-367
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• 1996

This paper deals with the design and evaluation of the robust controller for generator excitation system to improve the steady state and transient stabilities. The nonlinear characteristics of the system is treated as model uncertainties, and then the robust control techniques are introduced into the PSS design to take into account these uncertainties at the controller design stage. The performance of the designed controller is examined by extensive non-linear time domain simulation. It is shown that the performance of the robust controller is superior to that of the conventional PSS in all cases studied.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.443-447
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• 2001

In this paper, a robust sensorless vector control system for induction motors with a speed estimator and an uncertainty observer is presented. At first, the proposed speed estimator is based on the MRAS(Mode Reference Adaptive System) scheme and constructed with a simple fuzzy logic(FL) approach. The structure of the proposed FL estimator is very simple. The input of the FL is the rotor flux error difference between reference and adjustable model, and the output is the estimated incremental rotor speed Secondly, the unmodeled uncertainties such as parametric uncertainties and external load disturbances are modeled by a radial basis function network(RBFN). In the overal speed control system, the control inputs are composed with a norminal control input and a compensated control input, which are from RBFN observer output and the modeling error of the RBFN, repectively. The compensated control input is derived from Lyapunov unction approach. The simulation results are presented to show the validity of the proposed system.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.30 no.4
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• pp.329-340
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• 1994

This paper presents a dynamic compensation methodology for robust trajectory tracking control of uncertain robot manipulators. To improve tracking performance of the system, a full model-based feedforward compensation with continuous VS-type robust control is developed in this paper(i.e,. robust decentralized adaptive control scheme). Since possible bounds of uncertainties are unknown, the adaptive bounds of the robust control is used to directly estimate the uncertainty bounds(instead of estimating manipulator parameters as in centralized adaptive control0. The global stability and robustness issues of the proposed control algorithm have been investigated extensively and rigorously via a Lyapunov method. The presented control algorithm guarantees that all system responses are uniformly ultimately bounded. Thus, it is shown that the control system is evaluated to be highly robust with respect to significant uncertainties.

• Proceedings of the KIEE Conference
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• 2008.10b
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• pp.77-78
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• 2008

This paper presents to control speed of induction motors with uncertainties. We use an adaptive backstepping controller with fuzzy neural networks(FNNs) and model reference adaptive system(MRAS) at Indirect vector control method. The adaptive backstepping controller using FNNs can control speed of induction motors even we have a minimum of information. And this controller can be used to approximate most of uncertainties which are derived from unknown motor parameters, load torque such as disturbances. MRAS estimates to rotor resistance and also can find optimal flux to minimize power losses of Induction motor. Indirect vector PI current controller is used to keep rotor flux constant without measuring or estimating the rotor flux. Simulation and experiment results are verified the effectiveness of this proposed approach.