• Proceedings of the Korean Society of Precision Engineering Conference
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• 2001.04a
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• pp.177-180
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• 2001

This paper describes the calibration method and the evaluation method of relative expanded uncertainty for a multi-axis force/moment sensor. This sensor should be calibrated to be use in the industry. Now, the confidence of the calibration result is expressed with interference error. But it is no inaccurate, because an interference error, besides, a reproducibility error of the sensor, a error of this six-axis force/moment sensor calibrator, and so on. Thus, in order to accurately evaluate the relative expanded uncertainty of it, the concept of the uncertainty should be induced, and these errors must be contained in the relative expanded uncertainty. In this paper, the calibration method is exhibited and the evaluation method of the relative expanded uncertainty is also exhibited. And, a six-axis force/moment sensor was calibrated and the relative expanded uncertainty was evaluated.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.348-351
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• 1995

This note proposes a robust LQR method for systems with structured real parameter uncertainty based on Riccati equation approach. Emphasis is on the reduction of design conservatism in the sense of quadratic performance by utilizing the uncertainty structure. The class of uncertainty treated includes all the form of additive real parameter uncertainty, which has the multiple rank structure. To handle the structure of uncertainty, the scaling matrix with block diagonal structure is introduced. By changing the scaling matrix, all the possible set of uncertainty structures can be represented. Modified algebraic Riccati equation (MARE) is newly proposed to obtain a robust feedback control law, which makes the quadratic cost finite for an arbitrary scaling matrix. The remaining design freedom, that is, the scaling matrix is used for minimizing the upper bound of the quadratic cost for all possible set of uncertainties within the given bounds. A design example is shown to demonstrate the simplicity and the effectiveness of proposed method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.1
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• pp.17-25
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• 2005

This paper provides sufficient conditions for the robustness of Affine linear TFM(Transfer Function Matrix) MIMO (Multi-Input Multi-Output) uncertain systems based on Rosenbrock's DNA (Direct Nyquist Array). The parametric uncertainty is modeled through a Affine TFM MIMO description, and the unstructured uncertainty through a bounded perturbation of Affine polynomials. Gershgorin's theorem and concepts of diagonal dominance and GB(Gershgorin Bands) are extended to include model uncertainty. For this type of parametric robust performance we show robustness of the Affine TFM systems using Nyquist diagram and GB, DNA(Direct Nyquist Array). Multiloop PI/PB controllers can be tuned by using a modified version of the Ziegler-Nickels (ZN) relations. Simulation examples show the performance and efficiency of the proposed multiloop design method.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.504-506
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• 1999

In this paper, we propose a method which control parametric uncertainty systems using PID controller by fuzzy auto tuning. We get the error and the error change rate of plant output correspond to the initial value of parameter using the Ziegler-Nickols tuning and determine the new proportional gain$(K_p)$ and the integral time $(T_i)$ from fuzzy tuner by the error and error change rate of plant output as a membership function of fuzzy theory. The Fuzzy Auto-tuning algorithm for PID controller operate to adapt variable parameter of plant in parametric uncertainty systems. It is shown this method considerably improve the transient response at computer simulation.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.4
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• pp.329-334
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• 2010

This paper presents the design of a neural network based adaptive control for missile. Acceleration of missile by tail fin control cannot be controllable by DMI (Dynamic Model Inversion) directly because it is non-minimum phase system. To avoid the non-minimum phase system, dynamic model inversion is applied with output-redefinition method. In order to evaluate performance of the suggested controllers we selected the three cases such as control surface fail, control surface loss and wing loss for model uncertainty. The corresponding aerodynamic databases to the failure cases were calculated by using the Missile DATACOM. Using a high fidelity 6DOF simulation program of the missile the performance was evaluates.

• Journal of The Korean Society of Agricultural Engineers
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• v.49 no.4
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• pp.13-22
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• 2007

The uncertainty in water quality model predictions is inevitably high due to natural stochasticity, model uncertainty, and parameter uncertainty. An integrated modeling system under uncertainty was described and demonstrated for use in watershed management and receiving-water quality prediction. A watershed model (HSPF), a receiving water quality model (WASP), and a wetland model (NPS-WET) were incorporated into an integrated modeling system (modified-BASINS) and applied to the Hwaseong Reservoir watershed. Reservoir water quality was predicted using the calibrated integrated modeling system, and the deterministic integrated modeling output was useful for estimating mean water quality given future watershed conditions and assessing the spatial distribution of pollutant loads. A Monte Carlo simulation was used to investigate the effect of various uncertainties on output prediction. Without pollution control measures in the watershed, the concentrations of total nitrogen (T-N) and total phosphorous (T-P) in the Hwaseong Reservoir, considering uncertainty, would be less than about 4.8 and 0.26 mg 4.8 and 0.26 mg $L^{-1}$, respectively, with 95% confidence. The effects of two watershed management practices, a wastewater treatment plant (WWTP) and a constructed wetland (WETLAND), were evaluated. The combined scenario (WWTP + WETLAND) was the most effective at improving reservoir water quality, bringing concentrations of T-N and T-P in the Hwaseong Reservoir to less than 3.54 and 0.15 mg ${L^{-1}$, 26.7 and 42.9% improvements, respectively, with 95% confidence. Overall, the Monte Carlo simulation in the integrated modeling system was practical for estimating uncertainty and reliable in water quality prediction. The approach described here may allow decisions to be made based on probability and level of risk, and its application is recommended.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.2
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• pp.140-146
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• 2002

We consider a class of large-scale interconnected time delay systems and investigate a decentralized robust passive control problem. sufficient conditions for unforced interconnected uncertain systems with time delay to be robustly stable with extended strictly passivity is given in terms of algebraic Riccati inequality and linear matrix inequality. The decentralized robust passive control problem for norm-bounded and positive real uncertainty is shown to be converted to extended strictly positive real control problem for a modified system which contains neither time delay nor uncertainty.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.627-630
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• 2002

A methodology based on factorial design and Motto Carlo methods is developed and implemented for incorporating uncertainties within a multiphase subsurface flow and transport simulation system. Due to uncertainties in intrinsic permeability and longitudinal dispersivity, the predicted output is also uncertain based on the well-developed multiphase compositional simulator. The simulation results reveal that the uncertainties in input parameters pose considerable influences on the predicted output, and the mean and variance of permeability will have significant impacts on the modeling output. The proposed method offers an effective tool for evaluating uncertainty in multiphase flow simulation system.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.1-4
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• 1999

This paper presents a design scheme of robust parallel compensator for plants with multiple uncertainty, which realizes strict positive realness of the closed-loop system by using static output feedback. Further, an ap-proximate relation between the static output feedback control system with the proposed compensator and the PID$.$‥D$\^$r-1 control system is shown.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.647-649
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• 1998

This paper describes a robust stabilization of single input nonlinear systems with parametric uncertainty. We first investigate differential flatness of the nominal nonlinear systems. If a single input system is differentially flat, it possesses a flat output. And we define coordinate transformation functions via successively differentiating the flat output, and we also consider the robust fictitious controls at every differentiation of the flat output. In the new coordinates the nonlinear system is transformed into the Brunovsky normal form with matched uncertainty. With a robust control based on the Lyapunov method, the robust stabilization is achieved.