Subspace identification for MIMO systems in the presence of trends and outliers

Mikael Manngård; Jari Böling; Hannu Toivonen

doi:10.1016/B978-0-444-63965-3.50053-2

Subspace identification for MIMO systems in the presence of trends and outliers

Mikael Manngård, Jari Böling, Hannu Toivonen

Forskningsoutput: Kapitel i bok/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

3 Citeringar (Scopus)

Sammanfattning

In this paper we present a framework for subspace identification of multiple-input multiple output linear time-invariant systems from data corrupted by outliers and piece-wise linear trends. The subspace identification problem is formulated as a sparsity constrained rank minimization problem that is relaxed using the nuclear norm and the l1-norm. The proposed identification method has been validated on a simulated example and on a case study using data from a pilot-plant distillation column.

Originalspråk	Odefinierat/okänt
Titel på värdpublikation	27th European Symposium on Computer Aided Process Engineering
Redaktörer	Antonio Espuña, Moisès Graells, Luis Puigjaner
Förlag	Elsevier
Sidor	307–312
ISBN (elektroniskt)	978-0-444-63970-7
ISBN (tryckt)	978-0-444-63965-3
DOI	https://doi.org/10.1016/B978-0-444-63965-3.50053-2
Status	Publicerad - 2017
MoE-publikationstyp	A4 Artikel i en konferenspublikation
Evenemang	European Symposium on Computer Aided Process Engineering (ESCAPE) - 27th European Society of Computer-Aided Process Engineering (ESCAPE) Varaktighet: 1 okt. 2017 → 5 okt. 2017

Konferens

Konferens	European Symposium on Computer Aided Process Engineering (ESCAPE)
Period	01/10/17 → 05/10/17

Åtkomst av dokument

10.1016/B978-0-444-63965-3.50053-2

https://www.elsevier.com/books/27th-european-symposium-on-computer-aided-process-engineering/espuna/978-0-444-63965-3

Citera det här

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title = "Subspace identification for MIMO systems in the presence of trends and outliers",

abstract = "In this paper we present a framework for subspace identification of multiple-input multiple output linear time-invariant systems from data corrupted by outliers and piece-wise linear trends. The subspace identification problem is formulated as a sparsity constrained rank minimization problem that is relaxed using the nuclear norm and the l1-norm. The proposed identification method has been validated on a simulated example and on a case study using data from a pilot-plant distillation column.",

author = "Mikael Manng{\aa}rd and Jari B{\"o}ling and Hannu Toivonen",

note = "rt. Konferenspublikationen finns p{\aa} en minnespinne hos A.F.; European Symposium on Computer Aided Process Engineering (ESCAPE) ; Conference date: 01-10-2017 Through 05-10-2017",

year = "2017",

doi = "10.1016/B978-0-444-63965-3.50053-2",

language = "Odefinierat/ok{\"a}nt",

isbn = "978-0-444-63965-3",

pages = "307–312",

editor = "Antonio Espu{\~n}a and Mois{\`e}s Graells and Luis Puigjaner",

booktitle = "27th European Symposium on Computer Aided Process Engineering",

publisher = "Elsevier",

}

Manngård, M , Böling, J & Toivonen, H 2017, Subspace identification for MIMO systems in the presence of trends and outliers. i A Espuña, M Graells & L Puigjaner (red), 27th European Symposium on Computer Aided Process Engineering. Elsevier, s. 307–312, European Symposium on Computer Aided Process Engineering (ESCAPE), 01/10/17. https://doi.org/10.1016/B978-0-444-63965-3.50053-2

Subspace identification for MIMO systems in the presence of trends and outliers. / Manngård, Mikael ; Böling, Jari ; Toivonen, Hannu.
27th European Symposium on Computer Aided Process Engineering. red. / Antonio Espuña; Moisès Graells; Luis Puigjaner. Elsevier, 2017. s. 307–312.

Forskningsoutput: Kapitel i bok/konferenshandling › Konferensbidrag › Vetenskaplig › Peer review

TY - GEN

T1 - Subspace identification for MIMO systems in the presence of trends and outliers

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PY - 2017

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AB - In this paper we present a framework for subspace identification of multiple-input multiple output linear time-invariant systems from data corrupted by outliers and piece-wise linear trends. The subspace identification problem is formulated as a sparsity constrained rank minimization problem that is relaxed using the nuclear norm and the l1-norm. The proposed identification method has been validated on a simulated example and on a case study using data from a pilot-plant distillation column.

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