Model reduction for a class of linear dynamic systems

Shoji, F.F.; Abe, Kenichi; Takeda, Hiroshi

doi:10.1016/0016-0032(85)90017-1

Cited by 26 publications

(5 citation statements)

References 9 publications

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“…(4) by substituting Eq. (6) gives the reduced denominator coefficients as the solution of: ⋯ (8) (or) Pe = q in matrix vector form Eq. (8) is equivalent to equating all the significant time moments ( ) and markov parameters ( ) of ( ).…”

Section: Methodsmentioning

confidence: 99%

“…A serious drawback of this method is generating unstable reduced model for a stable higher order model. To overcome this problem Shoji et al [6] suggests using a least squares time moment to obtain a reduced transfer function denominator, and numerator by exact time moment matching. This method has been refined by Lucas and Beat [7], in which the linear shift point was about general point "a", where a≈(1-α) and -α is the real part of the smallest magnitude pole.…”

Section: Introductionmentioning

confidence: 99%

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Design of Controller for Large Scale Uncertain Systems via Reduces Order Model

Narasimhulu¹

2020

IJMTST

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In This paper, a method of designing the Controller for large scale uncertain systems. The Controller is designed via a reduced order model for a given high order system. An optimized reduced order model is derived with minimum ISE. The proposed method guarantees stability of the reduced model, if the original high order system is stable system. A PID controller is designed for the high order original systems through its low order model proposed. This paper presents an improvement to generalized least squares method of model order reduction. The improvement enhances the flexibility of the method with very little computational requirement. The reduction procedure is simple, efficient and always generates stable reduced models for the stable high order systems. The proposed method is illustrated with typical numerical examples taken from the literature and the results are compared with the other existing methods to show its superiority.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design of Controller for Large Scale Uncertain Systems via Reduces Order Model

Narasimhulu¹

2020

IJMTST

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“…To remedy this situation, several variants of the method have been proposed. One such technique [8] suggests using a least-squares time moment fit to obtain a reduced transfer function denominator, and then obtain the numerator by exact time matching. A suggestion to make this technique [8] less sensitive to the pole distribution of the original system, was proposed by Lucas and Beat [9], in which the linear shift point was about a general point 'a', where a~l-a) and -a is the real part of the smallest magnitude pole.…”

Section: Introductionmentioning

confidence: 99%

“…One such technique [8] suggests using a least-squares time moment fit to obtain a reduced transfer function denominator, and then obtain the numerator by exact time matching. A suggestion to make this technique [8] less sensitive to the pole distribution of the original system, was proposed by Lucas and Beat [9], in which the linear shift point was about a general point 'a', where a~l-a) and -a is the real part of the smallest magnitude pole. The method of model order reduction by least squares moment matching was generalized [10] by including the Markov parameters in the process to cope with a wider class of transfer functions.…”

Section: Introductionmentioning

confidence: 99%

Order reduction by error minimization technique

Ramesh

Nirmalkumar

Gurusamy

2008

2008 International Conference on Computing, Communication and Networking

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Modeling physical systems usually results in system of higher order whose order is greater than two. It is often desirable to approximate these models by reduced order models.A computationally simple approach is proposed for order reduction of linear time invariant discrete systems. The reduced order model is obtained by using the stability equation method and where the reduced order denominator polynomial constant term is obtained from the original higher order system transfer function. The proposed method assures the stability of the system under the reduced order model case. The validity of the proposed method is illustrated by solving few numerical examples and the results are compared with the existing techniques.

show abstract

“…A serious drawback of this method is generating unstable reduced model for a stable higher order model. To overcome this problem Shoji et al [6] suggests using a least squares time moment to obtain a reduced transfer function denominator, and numerator by exact time moment matching. This method has been refined by Lucas and Beat [7], in which the linear shift point was about general point 'a', where a≈(1α) and -α is the real part of the smallest magnitude pole.…”

Section: Introductionmentioning

confidence: 99%

Large Scale Model Order Reduction of Linear Time Invariant Dynamic System by Improvement of Generalised Least Squares Method

Vasu¹,

Murari²,

Mouli³

2012

Proceedings of the International Conference on Advances in Computer Science and Electronics Engineering

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This paper presents an improvement to generalised least squares method of model order reduction. The improvement enhances the flexibility of the method with very little computational requirement. The reduction procedure is simple, efficient and always generates stable reduced models for the stable high order systems. The proposed method is illustrated with typical numerical examples taken from the literature and the results are compared with the other existing methods to show its superiority.

show abstract

Model reduction for a class of linear dynamic systems

Cited by 26 publications

References 9 publications

Design of Controller for Large Scale Uncertain Systems via Reduces Order Model

Design of Controller for Large Scale Uncertain Systems via Reduces Order Model

Order reduction by error minimization technique

Large Scale Model Order Reduction of Linear Time Invariant Dynamic System by Improvement of Generalised Least Squares Method

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