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Transactions of the Institute of Systems, Control and Information Engineers Vol. 18 (2005), No. 11

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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  1. Vol. 35 (2022)

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  2. Vol. 34 (2021)

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  3. Vol. 33 (2020)

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  4. Vol. 32 (2019)

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  5. Vol. 31 (2018)

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  6. Vol. 30 (2017)

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  7. Vol. 29 (2016)

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  8. Vol. 28 (2015)

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  9. Vol. 27 (2014)

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  10. Vol. 26 (2013)

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  11. Vol. 25 (2012)

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  12. Vol. 24 (2011)

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  13. Vol. 23 (2010)

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  14. Vol. 22 (2009)

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  15. Vol. 21 (2008)

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  17. Vol. 19 (2006)

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  18. Vol. 18 (2005)

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  19. Vol. 17 (2004)

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  20. Vol. 16 (2003)

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  21. Vol. 15 (2002)

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  22. Vol. 14 (2001)

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  23. Vol. 13 (2000)

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  24. Vol. 12 (1999)

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  25. Vol. 11 (1998)

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  26. Vol. 10 (1997)

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  27. Vol. 9 (1996)

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  28. Vol. 8 (1995)

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  29. Vol. 7 (1994)

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  30. Vol. 6 (1993)

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  31. Vol. 5 (1992)

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  32. Vol. 4 (1991)

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  33. Vol. 3 (1990)

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  34. Vol. 2 (1989)

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  35. Vol. 1 (1988)

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Transactions of the Institute of Systems, Control and Information Engineers Vol. 18 (2005), No. 11

Sliding Mode Control for Descriptor Systems

Takashi MIYAZAKI, Shigeyuki HOSOE

pp. 377-385

DOI:

10.5687/iscie.18.377

Abstract

In this paper we consider sliding mode control for descriptor systems. We propose a control input that bring and constrain the trajectory to a switching hyper plane and a design method of the hyper plane. We reduce this design problem to a stabilizing problem of a descriptor system. This stabilizing problem is solved by a generalized algebraic Riccati equation that has a kind of optimality. We also provide a computation method by considering a generalized eigenvalue problem

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Sliding Mode Control for Descriptor Systems

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An Application of Lagrangian Relaxation Method to the Piston Delivery Scheduling Problem with Time-Windows

Tatsuya KOBAYASHI, Kazumiti NUMATA

pp. 386-392

DOI:

10.5687/iscie.18.386

Abstract

In this paper we address a piston delivery scheduling problem with time-window constraints. The feature of this problem is a piston delivery. In a piston delivery, total running cost (length) to deliver products to all customers doesn't change even if we use a lot of or a few trucks. But the number of trucks necessary to satisfy time-window constraints is varied by the delivery timing. One of main factors of the delivery cost is the number of trucks to be secured. The minimum (or near minimum) number of trucks necessary for a given delivery request is a matter of interest. We propose a new solution method using Lagrangian relaxation for this problem. Numerical experiments show that the proposed method produces solutions with 25-20% less trucks than those by simple heuristics.

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An Application of Lagrangian Relaxation Method to the Piston Delivery Scheduling Problem with Time-Windows

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An Algorithm for the Spectral Factorization of Unimodular Para-Hermitian Polynomial Matrices in Continuous Time

Osamu KANEKO, Paolo RAPISARDA, Kiyotsugu TAKABA

pp. 393-399

DOI:

10.5687/iscie.18.393

Abstract

In this paper, we address an algorithm for the spectral factorization of para-Hermitian unimodular polynomial matrices in the continuous time case. Most of the algorithms for the spectral factorizations of matrix polynomials depend on the existence of the roots of given polynomial matrices, so it is almost impossible to execute the spectral factorization of unimodular polynomial matrices. In this paper, we provide a new algorithm for the spectral factorization of unimodular polynomial matrices without the existence of the roots of polynomial matrices or the stability. The task one has to do is only to solve a linear matrix inequality consisting of the coefficients of a given unimodular matrix, which can be achieved easily by the use of numerical computation packages. The algorithm we present here is based on the property of the storage functions for the dissipative systems in which there always exists positive dissipated energy for the environment. This implies that the fundamental property in our algorithm is also a self-standing interesting result with respect to theoretical points of view. Finally, in order to show the validity of our results, we give an illustrative example with respect to numerical aspects.

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An Algorithm for the Spectral Factorization of Unimodular Para-Hermitian Polynomial Matrices in Continuous Time

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A New Parameter Tuning for Controllers Based on Least-Squares Method by using One-Shot Closed Loop Experimental Data

Osamu KANEKO, Kyoko YOSHIDA, Kazuyuki MATSUMOTO, Takao FUJII

pp. 400-409

DOI:

10.5687/iscie.18.400

Abstract

Recently, for the purpose of the minimization of the squared error between the desired and real experimental output of the plant, Iterative Feedback Tuning (IFT), Virtual Reference Feedback Tuning (VRFT), and Fictitous Reference Iterative Tuning (FRIT) were proposed as reasonable methods for tuning of the parameters of controllers. The first one requires many experiments for the updating step in Gauss-Newton method, while the later two methods requires only one-shot experimental data. Moreover, VRFT and FRIT have useful features in a complementary manner. From these backgrounds, this paper provides a new parameter tuning for controllers based on least-squares optimization by using one-shot closed loop experimental data. Here, we provide a basic idea of this approach and discuss the optimality of the obtained parameter. Moreover, we introduce the pre-filter so as to guarantee that the optimized parameter in the fictitious area corresponds to that in the real area. We also provide the algorithm of our method for enhancement of the use of this procedure. Finally we give an experimental example to illustrate the validity of our results.

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A New Parameter Tuning for Controllers Based on Least-Squares Method by using One-Shot Closed Loop Experimental Data

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Prediction of Linear Time-varying Stochastic Systems based on the Subspace Identification Method and its Recursive Algorithm

Kentaro KAMEYAMA, Akira OHSUMI

pp. 410-419

DOI:

10.5687/iscie.18.410

Abstract

In this paper, a prediction method is newly proposed for time-varying stochastic linear systems in the subspace identification framework. The key to this subspace-based prediction is to regard the change of the extended observability matrix yielded by the time-varying parameters of system as the rotation of the principal vectors that span the basis of the signal subspace. The rotation rate is evaluated from the angle between the past and current signal subspaces, and the future signal subspace is predicted by rotating the current subspace. A recursive algorithm is derived and its efficacy is tested by simulation experiments.

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Prediction of Linear Time-varying Stochastic Systems based on the Subspace Identification Method and its Recursive Algorithm

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