• Journal of Institute of Control, Robotics and Systems
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• v.8 no.5
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• pp.351-356
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• 2002

This papaer deals with a model reduction problem for linear systems with time varying parameters. For this problem, a controllability Grammian and an observability Grammian are introduced and computed by solving linear matrix inequalities. Using the controllability/observability Grammian, a balanced state space realization for linear parameter varying systems is obtained. From the balanced state space realization, a reduced model can be obtained by truncating not only states but also time varying parameters and an upper bound of the model reduction error is derived as well.

• Journal of applied mathematics & informatics
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• v.8 no.1
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• pp.297-303
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• 2001

In this paper, the problem of the stability analysis for a class of linear neutral systems with time-varying delay is investigated. Using the Lyapunov method, a delay-dependent sufficient condition for asymptotic stability of the systems in terms of linear matrix inequalities (LMIs) is presented. The LMIs can be easily solved by various convex optimization algorithms.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.12
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• pp.2284-2291
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• 2010

In this paper, improved stability criteria for linear systems with time-varying delays are proposed. By constructing a new Lyapunov functional, novel stability criteria are established in terms of linear matrix inequalities (LMIs). Two numerical examples are carried out to support the effectiveness of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.181-186
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• 2009

In this paper, the stability analysis for uncertain dynamic systems with time-varying delays is considered. By constructing a new Lyapunov functional, a novel stability criterion is established in terms of linear matrix inequalities (LMIs). Two numerical examples are carried out to support the effectiveness of the proposed method.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.50 no.8
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• pp.384-391
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• 2001

This paper considers the problem of identifying the time-varying parameters of Bilinear systems. The Parameters, in this paper, are identified by using the EBPOMs(Extended Block Pulse Operational Matrices) which can reduce the burden of operation and the volume of error caused by matrices multiplication.

• 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.58 no.11
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• pp.2261-2265
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• 2009

In this paper, the problem of delay-dependent stability of uncertain stochastic systems with time-varying delay is considered. The uncertainties are assumed to be norm-bounded. Based on the Lyapunov stability theory, new delay-dependent stability criteria for the system are derived in terms of LMI(linear matrix inequality). Two numerical examples are given to show the effectiveness of proposed method.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.755-757
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• 1999

The operational properties of BPF(block-pulse functions) are much applied to the analysis of time-varying linear systems. The integral operational matrix of BPF converts the systems in the form of the differential equation into the algebraic problems. But the errors caused by using the integral operational matrix make it difficult that we exactly analyze time-varying linear systems. So, in this paper, to analyze time-varying linear systems we had used the recursive algorithm derived from the new integral operational matrix. And the usefulness of the proposed method is verified by the example.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.78.5-78
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• 2002

We propose an evolutionary reinforcement learning (RL) system with time-varying parameters that can deal with a dynamic environment. The proposed system has three characteristics: 1) It can deal easily with a dynamic environment by using time-varying parameters; 2) The division of state space is acquired evolutionarily by genetic algorithm (GA); 3) One does not have to design the rules constructing an agent in advance. So far many RL systems have been proposed. These systems adjust constant or non time-varying parameters; by those systems it is difficult to realize appropriate behavior in complex and dynamic environment. Hence, we propose the RL system whose parameters can vary temporally. T...