A new look at model reduction by moment matching for linear systems

2007 ◽  
Author(s):  
Alessandro Astolfi
Keyword(s):  
Model Reduction ◽  
Linear Systems ◽  
Moment Matching ◽  
New Look

Moment matching model reduction for multiratc linear systems

1990 ◽  
Vol 52 (6) ◽  
pp. 1279-1294 ◽  
Author(s):  
D. WILLIAMSON ◽  
R. E. SKELTON ◽  
G. ZHU
Keyword(s):  
Model Reduction ◽  
Linear Systems ◽  
Moment Matching ◽  
Matching Model

scholarly journals Structured model reduction of interconnected linear systems based on singular perturbation

2013 ◽  
Author(s):  
Takayuki Ishizaki ◽  
Henrik Sandberg ◽  
Karl Henrik Johansson ◽  
Kenji Kashima ◽  
Jun-ichi Imura ◽  
...  
Keyword(s):  
Singular Perturbation ◽  
Model Reduction ◽  
Linear Systems ◽  
Structured Model

Data-driven nonlinear model reduction by moment-matching for the ISWEC system

2021 ◽  
Author(s):  
Nicolas Faedo ◽  
Francisco Javier Dores Piuma ◽  
Giuseppe Giorgi ◽  
Giovanni Bracco ◽  
John V. Ringwood ◽  
...  
Keyword(s):  
Model Reduction ◽  
Nonlinear Model ◽  
Data Driven ◽  
Moment Matching ◽  
Nonlinear Model Reduction

A new model order reduction method for the design of compensator by using moment matching algorithm

2019 ◽  
Vol 42 (3) ◽  
pp. 472-484 ◽  
Author(s):  
Arvind Kumar Prajapati ◽  
Rajendra Prasad
Keyword(s):  
Model Reduction ◽  
Lower Order ◽  
Large Scale ◽  
Order Reduction ◽  
Order System ◽  
Moment Matching ◽  
Matching Algorithm ◽  
New Model ◽  
Factor Division Algorithm ◽  
Lower Order System

The aim of this paper is the construction of a new model reduction technique for large scale stable linear dynamic systems. It is principally focused on the dominant modes and time moments retention. This reduction implicates the translation of the overall important features confined in the large scale complete order model into the lower order system, allowing the computation of approximant denominator by using generalized pole clustering method. The approximant numerator is obtained by means of the factor division algorithm. As a result, a lower order system is obtained. To demonstrate its effectiveness, to highlight some fundamental of its features, and to accomplish its accuracy, a comparative study is done. Two standard numerical examples are taken, where approximant model computed by the proposed method is compared with the reduced order models computed from the recently proposed methods as well as well-known model reduction schemes. The paper is also emphasized on the design of compensator by using moment matching algorithm with the help of the reduced model. The design of compensator is validated and illustrated with the help of a standard numerical example taken from the literature.

Model Reduction of Linear Systems with Random Parameters

2013 ◽  
Vol 46 (11) ◽  
pp. 512-517 ◽  
Author(s):  
L. Nechak ◽  
H-F. Raynaud ◽  
C. Kulcsár
Keyword(s):  
Model Reduction ◽  
Linear Systems ◽  
Random Parameters
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