Advanced

Semidefinite Hankel-type Model Reduction Based on Frequency Response Matching

Sootla, Aivar LU (2012) In IEEE Transactions on Automatic Control
Abstract
This paper is dedicated to model order reduction of linear time-invariant systems. The main contribution of this paper is the derivation of two scalable stability-preserving model reduction algorithms. Both algorithms constitute a development of a recently proposed model reduction method. The algorithms perform a curve fitting procedure using frequency response samples of a model and semidefinite programming methods. Computation of these samples can be done efficiently even for large scale models. Both algorithms are obtained from a reformulation of the model reduction problem. One proposes a semidefinite relaxation, while the other is an iterative semidefinite approach. The relaxation approach is similar to Hankel model reduction, which... (More)
This paper is dedicated to model order reduction of linear time-invariant systems. The main contribution of this paper is the derivation of two scalable stability-preserving model reduction algorithms. Both algorithms constitute a development of a recently proposed model reduction method. The algorithms perform a curve fitting procedure using frequency response samples of a model and semidefinite programming methods. Computation of these samples can be done efficiently even for large scale models. Both algorithms are obtained from a reformulation of the model reduction problem. One proposes a semidefinite relaxation, while the other is an iterative semidefinite approach. The relaxation approach is similar to Hankel model reduction, which is a well-known and established method in the control literature. Due to this resemblance, the accuracy of approximation is also similar to the one of Hankel model reduction. An appealing quality of the proposed algorithms is the ability to easily perform extensions, e.g., introduce frequency-weighting, positive-real and bounded-real constraints. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
submitted
subject
keywords
Reduced order modeling, Model/controller reduction, Optimization, Semidefinite programming
in
IEEE Transactions on Automatic Control
publisher
IEEE--Institute of Electrical and Electronics Engineers Inc.
ISSN
0018-9286
project
LCCC
language
English
LU publication?
yes
id
55e04a9b-d4f8-4100-964e-7599203784fa (old id 2544108)
date added to LUP
2012-05-25 11:47:59
date last changed
2016-04-16 11:20:50
@article{55e04a9b-d4f8-4100-964e-7599203784fa,
  abstract     = {This paper is dedicated to model order reduction of linear time-invariant systems. The main contribution of this paper is the derivation of two scalable stability-preserving model reduction algorithms. Both algorithms constitute a development of a recently proposed model reduction method. The algorithms perform a curve fitting procedure using frequency response samples of a model and semidefinite programming methods. Computation of these samples can be done efficiently even for large scale models. Both algorithms are obtained from a reformulation of the model reduction problem. One proposes a semidefinite relaxation, while the other is an iterative semidefinite approach. The relaxation approach is similar to Hankel model reduction, which is a well-known and established method in the control literature. Due to this resemblance, the accuracy of approximation is also similar to the one of Hankel model reduction. An appealing quality of the proposed algorithms is the ability to easily perform extensions, e.g., introduce frequency-weighting, positive-real and bounded-real constraints.},
  author       = {Sootla, Aivar},
  issn         = {0018-9286},
  keyword      = {Reduced order modeling,Model/controller reduction,Optimization,Semidefinite programming},
  language     = {eng},
  publisher    = {IEEE--Institute of Electrical and Electronics Engineers Inc.},
  series       = {IEEE Transactions on Automatic Control},
  title        = {Semidefinite Hankel-type Model Reduction Based on Frequency Response Matching},
  year         = {2012},
}