Abstract
— Modelling of complex systems comes with higher
complexity and computational costs. Model order reduction
enables better models to be exploited by control, diagnosis and
prognosis algorithms. Effective model order reduction requires
efficient methods to generate reduced order models. This is
where the use of Krylov subspaces for model order reduction is
of great advantage, especially when associated with high order
bilinear model approximations of high order nonlinear models.
This paper demonstrates the use of an Improved Phillips
type projection for a one-sided Krylov subspace projection
for reducing bilinear models. A new multimoment matching
analysis of the proposed model reduction scheme is provided,
and compared to some existing results in the literature
complexity and computational costs. Model order reduction
enables better models to be exploited by control, diagnosis and
prognosis algorithms. Effective model order reduction requires
efficient methods to generate reduced order models. This is
where the use of Krylov subspaces for model order reduction is
of great advantage, especially when associated with high order
bilinear model approximations of high order nonlinear models.
This paper demonstrates the use of an Improved Phillips
type projection for a one-sided Krylov subspace projection
for reducing bilinear models. A new multimoment matching
analysis of the proposed model reduction scheme is provided,
and compared to some existing results in the literature
Original language | English |
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Pages | 2599-2604 |
Number of pages | 6 |
Publication status | Published - 15 Jun 2018 |
Event | 2018 European Control Conference - Limassol, Limassol, Cyprus Duration: 12 Jun 2018 → 15 Jun 2018 Conference number: 16 https://controls.papercept.net/conferences/conferences/ECC18/program/ECC18_ContentListWeb_4.html |
Conference
Conference | 2018 European Control Conference |
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Abbreviated title | ECC 18 |
Country/Territory | Cyprus |
City | Limassol |
Period | 12/06/18 → 15/06/18 |
Internet address |