• Journal of Electrical Engineering and Technology
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• v.6 no.5
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• pp.713-722
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• 2011

In the present paper, the problem of stability analysis for linear systems with interval time-varying delays is considered. By introducing a new Lyapunov-Krasovskii functional, new stability criteria are derived in terms of linear matrix inequalities (LMIs). Two numerical examples are given to show the superiority of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.9
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• pp.1748-1753
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• 2011

In this paper, we consider the stability of linear systems with an interval time-varying delay. It is known that the adoption of decomposition of delay improves the stability result. For the interval time-delay case, they applied it to the interval of time-delay and got less conservative results. Our basic idea is to apply the general decomposition to the low limit of delay as well as interval of time-delay. Based on this idea, by using the modified Lyapunov-Krasovskii functional and newly derived Lemma, we present a less conservative stability criterion expressed as in the form of linear matrix inequality(LMI). Finally, we show, by well-known two examples, that our result is less conservative than the recent results.

• Journal of Electrical Engineering and Technology
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• v.8 no.6
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• pp.1542-1550
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• 2013

This paper presents new results on delay-dependent global exponential stability for uncertain linear systems with interval time-varying delay. Based on Lyapunov-Krasovskii functional approach, some novel delay-dependent stability criteria are derived in terms of linear matrix inequalities (LMIs) involving the minimum and maximum delay bounds. By using delay-partitioning method and the lower bound lemma, less conservative results are obtained with fewer decision variables than the existing ones. Numerical examples are given to illustrate the usefulness and effectiveness of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.5
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• pp.673-678
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• 2013

This paper considers the stability of linear systems having an interval time-varying delay using a switched system approach. The time-delay system is converted to the switched system equivalently, and then a stability criterion in the form of linear matrix inequality(LMI) is derived by using a parameter dependent Lyapunov-Krosovskii function(PD-LKF). In constructing a PD-LKF, the decomposition is employed for delay free intervals, and the reduction of conservatism is shown analytically as the number of decomposition increases. Finally, two well-known numerical examples are given to show the reduction of conservatism compared to the recent results.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.8
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• pp.462-470
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• 2003

This paper presents an algorithm of identifying parametric uncertainty by way of an interval model. For a given set of frequency response data from an uncertain linear SISO system of which the upper and the lower bounds of both magnitude and phase responses are represented, the proposed algorithm consists of two main parts: first, the nominal model is identified by using Least Square Estimation (LSE), and then an interval model is constructed by expanding the extremal properties of interval systems, so that tightly enclose the given envelopes within those of interval model. Two numerical examples are given to demonstrate and verify the developed algorithm. The identified interval model can be used for evaluating the worst case performance and stability margins against parametric uncertainty by using some extremal properties on interval systems.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.3
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• pp.457-462
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• 2016

In this paper, we consider the stability of time-delayed linear systems. To derive an LMI form of result, the integral inequality is essential, and Jensen's integral inequality was the best in the last two decades until Seuret's integral inequality is appeared recently. However, these two are proportional to the inverse of integration interval, so another integral inequality is needed to make it in the form of LMI. In this paper, we derive an integral inequality which is proportional to the integration interval which can be easily converted into LMI form without any other inequality. Also, it is shown that Seuret's integral inequality is a special case of our result. Next, based on this new integral inequality, we derive a stability condition in the form of LMI. Finally, we show, by well-known two examples, that our result is less conservative than the recent results.

• Coupled systems mechanics
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• v.1 no.4
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• pp.345-360
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• 2012

In this article we have presented a unique representation for interval arithmetic. The traditional interval arithmetic is transformed into crisp by symbolic parameterization. Then the proposed interval arithmetic is extended for fuzzy numbers and this fuzzy arithmetic is used as a tool for uncertain finite element method. In general, the fuzzy finite element converts the governing differential equations into fuzzy algebraic equations. Fuzzy algebraic equations either give a fuzzy eigenvalue problem or a fuzzy system of linear equations. The proposed methods have been used to solve a test problem namely heat conduction problem along with fuzzy finite element method to see the efficacy and powerfulness of the methodology. As such a coupled set of fuzzy linear equations are obtained. These coupled fuzzy linear equations have been solved by two techniques such as by fuzzy iteration method and fuzzy eigenvalue method. Obtained results are compared and it has seen that the proposed methods are reliable and may be applicable to other heat conduction problems too.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.303-306
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• 1995

In this study we present the inverse correlation method to select the exploratory variables, while Sugeno used RC method in his paper[6] We assume linear model with measurement error variables as in Fuller's Book[9]. we provide possibilistic linear model and predict the fuzzy response variable in case of fuzzy exploratory variables. By plotting data we can divide them for piecewise plane and provide the piecwise possibilistic linear model. If the exploratory variable is fuzzy trapezoidal variable or interval variable, then we estimate fuzzy trapezoidal variable or interval variable, then we estimate fuzzy trapezoidal response variable respondent to it. We will illustrate using Nonlinear System data in Sugeno's paper

• Journal of the Korean Society for Precision Engineering
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• v.17 no.12
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• pp.121-128
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• 2000

The scope of this paper is focused to the systems which have the time period and they should be necessarily studied in the sense of stability and design method of controller to stabilize the orignal unstable systems. In general, the time periodic systems or the systems having same motions during certain time interval are easily found in rotating motion device, i.e., satellite or helicopter and widely used in factory automation systems. The characteristics of the selected dynamic systems are analyzed with the new stability concept and stabilization control method based on Lyapunov direct method. The new method from Lyapunov stability criteria which satisfies the energy convergence is studied with linear algebraic method. And the numerical procedures are developed with computational programming method to apply to the practical linear periodic systems. The results from this paper demonstrate the usefulness in analysis of the asymptotic stability and stabilization of the unstable linear periodic system by using the developed simulation procedures.

• Communications of the Korean Mathematical Society
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• v.22 no.3
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• pp.475-485
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• 2007

We construct high-degree interpolation rules using not only pointwise values of a function but also of its derivatives up to the p-th order at equally spaced nodes on a closed and bounded interval of interest by introducing a linear functional from which we produce systems of linear equations. The linear systems will lead to a conclusion that the rules are uniquely determined for the nodes. An example is provided to compare the rules with the classical interpolating polynomials.