Identification of externally positive systems

Christian Grussler; Jack Umenberger; Ian R. Manchester

doi:10.1109/CDC.2017.8264646

Identification of externally positive systems

Christian Grussler, Jack Umenberger, Ian R. Manchester

Department of Automatic Control

University of Sydney

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

Abstract

We consider identification of externally positive linear discrete-time systems from input/output data. The proposed method is formulated as a semidefinite program, and is guaranteed to identify models that are ellipsoidal cone-invariant and, consequently, externally positive. We demonstrate empirically that this cone-invariance approach can significantly reduce the conservatism associated with methods that enforce internal positivity as a sufficient condition for external positivity.

Original language	English
Title of host publication	2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
Publisher	IEEE - Institute of Electrical and Electronics Engineers Inc.
Pages	6549-6554
Number of pages	6
Volume	2018-January
ISBN (Electronic)	9781509028733
DOIs	https://doi.org/10.1109/CDC.2017.8264646
Publication status	Published - 2018 Jan 18
Event	56th IEEE Annual Conference on Decision and Control, CDC 2017 - Melbourne, Australia Duration: 2017 Dec 12 → 2017 Dec 15 Conference number: 56 http://cdc2017.ieeecss.org/

Conference

Conference	56th IEEE Annual Conference on Decision and Control, CDC 2017
Abbreviated title	CDC 2017
Country/Territory	Australia
City	Melbourne
Period	2017/12/12 → 2017/12/15
Internet address	http://cdc2017.ieeecss.org/

Subject classification (UKÄ)

Control Engineering

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10.1109/CDC.2017.8264646

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title = "Identification of externally positive systems",

abstract = "We consider identification of externally positive linear discrete-time systems from input/output data. The proposed method is formulated as a semidefinite program, and is guaranteed to identify models that are ellipsoidal cone-invariant and, consequently, externally positive. We demonstrate empirically that this cone-invariance approach can significantly reduce the conservatism associated with methods that enforce internal positivity as a sufficient condition for external positivity.",

author = "Christian Grussler and Jack Umenberger and Manchester, {Ian R.}",

year = "2018",

month = jan,

day = "18",

doi = "10.1109/CDC.2017.8264646",

language = "English",

volume = "2018-January",

pages = "6549--6554",

booktitle = "2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017",

publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "56th IEEE Annual Conference on Decision and Control, CDC 2017, CDC 2017 ; Conference date: 12-12-2017 Through 15-12-2017",

url = "http://cdc2017.ieeecss.org/",

}

Grussler, C, Umenberger, J & Manchester, IR 2018, Identification of externally positive systems. in 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017. vol. 2018-January, IEEE - Institute of Electrical and Electronics Engineers Inc., pp. 6549-6554, 56th IEEE Annual Conference on Decision and Control, CDC 2017, Melbourne, Australia, 2017/12/12. https://doi.org/10.1109/CDC.2017.8264646

Identification of externally positive systems. / Grussler, Christian; Umenberger, Jack; Manchester, Ian R.
2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017. Vol. 2018-January IEEE - Institute of Electrical and Electronics Engineers Inc., 2018. p. 6549-6554.

Research output: Chapter in Book/Report/Conference proceeding › Paper in conference proceeding › peer-review

TY - GEN

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AU - Umenberger, Jack

AU - Manchester, Ian R.

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PY - 2018/1/18

Y1 - 2018/1/18

N2 - We consider identification of externally positive linear discrete-time systems from input/output data. The proposed method is formulated as a semidefinite program, and is guaranteed to identify models that are ellipsoidal cone-invariant and, consequently, externally positive. We demonstrate empirically that this cone-invariance approach can significantly reduce the conservatism associated with methods that enforce internal positivity as a sufficient condition for external positivity.

AB - We consider identification of externally positive linear discrete-time systems from input/output data. The proposed method is formulated as a semidefinite program, and is guaranteed to identify models that are ellipsoidal cone-invariant and, consequently, externally positive. We demonstrate empirically that this cone-invariance approach can significantly reduce the conservatism associated with methods that enforce internal positivity as a sufficient condition for external positivity.

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U2 - 10.1109/CDC.2017.8264646

DO - 10.1109/CDC.2017.8264646

M3 - Paper in conference proceeding

AN - SCOPUS:85046425492

VL - 2018-January

SP - 6549

EP - 6554

BT - 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

T2 - 56th IEEE Annual Conference on Decision and Control, CDC 2017

Y2 - 12 December 2017 through 15 December 2017

ER -

Identification of externally positive systems

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Conference

Subject classification (UKÄ)

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