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Transactions of the Institute of Systems, Control and Information Engineers Vol. 23 (2010), No. 5

ISIJ International
belloff
ONLINE ISSN: 2185-811X
PRINT ISSN: 1342-5668
Publisher: THE INSTITUTE OF SYSTEMS, CONTROL AND INFORMATION ENGINEERS (ISCIE)

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  27. Vol. 11 (1998)

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  28. Vol. 10 (1997)

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  29. Vol. 9 (1996)

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  30. Vol. 8 (1995)

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  31. Vol. 7 (1994)

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  32. Vol. 6 (1993)

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  33. Vol. 5 (1992)

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  34. Vol. 4 (1991)

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  35. Vol. 3 (1990)

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  36. Vol. 2 (1989)

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  37. Vol. 1 (1988)

    1. No.7
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    3. No.5
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Keyword Ranking

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Transactions of the Institute of Systems, Control and Information Engineers Vol. 23 (2010), No. 5

The Deadlock Recovery Problem in the AGV System with the Ladder Guidepath Layout and its Computational Complexity

Kenji Koizumi, Shigeru Masuyama

pp. 91-104

DOI:

10.5687/iscie.23.91

Abstract

This paper proposes the minimum time deadlock recovery problem in the AGV system with the ladder guidepath layout(DRPL, for short) and analyzes its computational complexity. In order to analyze the computational complexity, this paper introduces the decision problem version of DRPL to ask whether all deadlocks in the AGV system are recoverable within predetermined time, and NP-hardness in a special case of the problem is proved. Moreover, the condition by which the problem becomes NP-hard when the AGV system has a ladder guidepath layout is clarified, and we propose a polynomial time algorithm that solves the optimization problem version of this problem whenever the problem in the ladder guidepath layout is not NP-hard.

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The Deadlock Recovery Problem in the AGV System with the Ladder Guidepath Layout and its Computational Complexity

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Ris

Feedback Error Learning by means of the Pre-filter State

Makoto Noguchi, Kenji Sugimoto

pp. 105-110

DOI:

10.5687/iscie.23.105

Abstract

This paper proposes a scheme for the Feedback Error Learning (FEL). Conventional FEL schemes apply a pre-filter to the reference signal, in order to compensate the delay (relative degree) of the plant. In this case, however, the pre-filter parameters, which is known, have to be learned as well as the plant parameters. The proposed method improves this redundancy by using the pre-filter state. Simulation results illustrate the effectiveness of the proposed method.

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Feedback Error Learning by means of the Pre-filter State

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