• Journal of Computing Science and Engineering
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• v.2 no.2
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• pp.200-219
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• 2008

The effective management of uncertainty is one of the most fundamental problems in medical decision making. According to the literatures review, most medical decision models rely on point estimates for input parameters. However, it is natural that they should be interested in the relationship between changes in those values and subsequent changes in model output. Therefore, the purpose of this study is to identify the ranges of numerical values for which each option will be most efficient with respect to the input parameters. The Nonhomogeneous Poisson Process(NHPP) was used for describing the behavior of aging chronic diseases. Three kind of failure models (linear, exponential, and power law) were considered, and each of these failure models was studied under the assumptions of unknown scale factor and known aging rate, known scale factor and unknown aging rate, and unknown scale factor and unknown aging rate, respectively. In addition, this study illustrated developed method with an analysis of data from a trial of immunotherapy in the treatment of chronic Granulomatous disease. Finally, the proposed design of Bayesian value of information analysis facilitates the effective use of the computing capability of computers and provides a systematic way to integrate the expert's opinions and the sampling information which will furnish decision makers with valuable support for quality medical decision making.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1589-1592
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• 1997

Among the prerequisite abilities (perception of environment, path planning and position estimation) of an autonomous mobile robot, position estimation has been seldom studied by mobile robot researchers. In most cases, conventional positioin estimation has been performed by placing landmarks or giving the entrire environmental information in advance. Unlikely to the conventional ones, the study addresses a new method that the robot itself can select distinctive features in the environment and save them as landmarks without any a priori knowledge, which can maximize the autonomous behavior of the robot. First, an orjentaion probaility model is applied to construct a lcoal map of robot's surrounding. The feature of the object in the map is then extracted and the map is saved as landmark. Also, presented is the position estimation method that utilizes the correspondence between landmarks and current local map. In dong this, the uncertainty of the robot's current positioin is estimated in order to select the corresponding landmark stored in the previous steps. The usefulness of all these approaches are illustrated with the results porduced by a real robot equipped with ultrasonic sensors.

• International Journal of Control, Automation, and Systems
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• v.4 no.4
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• pp.495-505
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• 2006

A class of robust control for flexible joint manipulators with nonlinearity mismatched uncertainty is designed based on Lyapunov approach. The uncertainties are unknown but their values are within certain prescribed sets. No statistic information of the uncertainties is imposed. The control which utilizes state transformation via virtual control is proposed. The resulting robust control guarantees practical stability for the transformed system and later the stability for the original system is proved. The designed robust control is implemented by experiments in a 2-link flexible joint manipulator.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.9
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• pp.658-663
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• 2003

In deterministic design of feedback controllers for uncertain dynamic systems, the upper bound of the uncertainty is very important to guarantee the stability of the closed loop system. In this paper, we assume that the upper bound of the uncertainty is formulated using a Fredholm integral equation of the first kind, that is, an integral of the product of a predefined kernel with an unknown influence function. We propose an adaptation law that is capable of estimating this upper bound. Using this adaptive upper bound, we design an adaptive variable structure control (AVSC), which guarantees asymptotic stability/ultimate boundedness of uncertain dynamic systems. The illustrative example shows the proposed AVSC is effective for uncertain dynamic systems.

• Transactions on Control, Automation and Systems Engineering
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• v.2 no.4
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• pp.255-261
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• 2000

This paper presents the results of the robust H(sub)$\infty$ FIR filtering for a class of nonlinear continuous time-varying systems subject to real norm-bounded parameter uncertainty and know Lipschitz nonlinearity under sampled measurements. We address the problem of designing filters, using sampled measurements, which guarantee a prescribed H(sub)$\infty$ performance in continuous time-varying context, irrespective of the parameter uncertainty and unknown initial states. The infinite horizon causal H(sub)$\infty$FIR filter are investigated using the finite moving horizon in terms of two Riccati equations with finite discrete jumps.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.4
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• pp.324-329
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• 2002

In this paper, the guaranteed cost filtering design method for linear time delay systems with convex bounded uncertainties in discrete-time case is presented. The uncertain parameters are assumed to be unknown but belonging to known convex compact set of polytotype less conservative than norm bounded parameter uncertainty. The main purpose is to design a stable filter which minimizes the guaranteed cost. The sufficient condition for the existence of filter, the guaranteed cost filter design method, and the upper bound of the guaranteed cost are proposed. Since the proposed sufficient conditions are LMI(linear matrix inequality) forms in terms of all finding variables, all solutions can be obtained simultaneously by means of powerful convex programming tools with global convergence assured. Finally, a numerical example is given to check the validity of the proposed method.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1509-1510
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• 2008

In this paper, we propose a robust adaptive controller for induction motors with uncertainties using nonlinear disturbance observer(NDO). The proposed NDO is applied to estimate the time varying lumped uncertainty which are derived from unknown motor parameters and load torque, but NDO error does not converge to zero since the derivate of lumped uncertainty is not zero. Then the high order neural networks(HONN) is presented to estimate the NDO error such that the rotor speed to converge to a small neighborhood of the desired trajectory. Rotor flux and inverse time constant are estimated by the sliding mode adaptive flux observer. Simulation results are provided to verify the effectiveness of the proposed approach.

• Proceedings of the KIEE Conference
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• 1997.07b
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• pp.418-421
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• 1997

A control synthesis scheme is presented for nonlinear single-input-single-output (SISO) systems with parametric uncertainty which have completely unstable zero dynamics. The approach involves the derivation of an input-output linearizing control law which achieves internal stability for a nonlinear minimum phase approximation to the original system using Fliess normal form. A vector of unknown constant parameters is also considered. A Lyapunov-based additional control law is shown to stabilize the full system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1996.10a
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• pp.158-161
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• 1996

A robust adaptive tracking control architecture is proposed for a class of continuous-time nonlinear dynamic systems for which an explicit linear parameterization of the uncertainty in the dynamics is either unknown or impossible. The architecture employs an adaptive fuzzy system to compensate for the uncertainty of the plant. In order to improve the robustness under approximation errors and disturbances, the proposed architecture includes deadzone in adaptation laws. Unlike the previously proposed schemes, the magnitude of approximate errors and disturbances is not required in the determination of the deadzone size, since it is estimated using the adaptation law. The proposed algorithm is proven to be globally stable in the Lyapunov sense, with tracking errors converging to the proposed architecture.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.2
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• pp.127-134
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• 2008

In this paper, we propose a robust adaptive backstepping control of induction motors with uncertainties using nonlinear disturbance observer(NDO). The proposed NDO is applied to estimate the time-varying lumped uncertainty which are derived from unknown motor parameters and load torque, but NDO error does not converge to zero since the derivate of lumped uncertainty is not zero. Then the fuzzy neural network(FNN) is presented to estimate the NDO error such that the rotor speed to converge to a small neighborhood of the desired trajectory. Rotor flux and inverse time constant are estimated by the sliding mode adaptive flux observer. Simulation results are provided to verify the effectiveness of the proposed approach.