A Model Decomposition Framework for LPV Systems

Tamas Luspay, Tamas Peni, Peter J Seiler Jr, Balint Vanek

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The paper proposes a systematic framework for efficient decomposition of Linear Parameter Varying (LPV) systems. Our aim is to reveal the topological structure of the system, to facilitate various analysis and synthesis methods. For this purpose, first we extend the notion of Gramian based interaction measure for parameter dependent systems. However, the metric is based on the solution of an iterative optimization, subject to Linear Matrix Inequality (LMI) constraints. Therefore, in order to ease the computation burden, we apply a modal decomposition to the system. A simple structured Gramian computation is introduced, with fast conic programming. The proposed methodology is illustrated by a numerical example.

Original languageEnglish (US)
Title of host publication2018 IEEE Conference on Decision and Control, CDC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5898-5903
Number of pages6
ISBN (Electronic)9781538613955
DOIs
StatePublished - Jan 18 2019
Event57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States
Duration: Dec 17 2018Dec 19 2018

Publication series

NameProceedings of the IEEE Conference on Decision and Control
Volume2018-December
ISSN (Print)0743-1546

Conference

Conference57th IEEE Conference on Decision and Control, CDC 2018
CountryUnited States
CityMiami
Period12/17/1812/19/18

Fingerprint

Linear Parameter-varying Systems
Decomposition
Decompose
Linear matrix inequalities
Conic Programming
Topological Structure
Inequality Constraints
Matrix Inequality
Linear Inequalities
Model
Synthesis
Metric
Numerical Examples
Optimization
Methodology
Dependent
Interaction
Framework

Cite this

Luspay, T., Peni, T., Seiler Jr, P. J., & Vanek, B. (2019). A Model Decomposition Framework for LPV Systems. In 2018 IEEE Conference on Decision and Control, CDC 2018 (pp. 5898-5903). [8618676] (Proceedings of the IEEE Conference on Decision and Control; Vol. 2018-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2018.8618676

A Model Decomposition Framework for LPV Systems. / Luspay, Tamas; Peni, Tamas; Seiler Jr, Peter J; Vanek, Balint.

2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. p. 5898-5903 8618676 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2018-December).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Luspay, T, Peni, T, Seiler Jr, PJ & Vanek, B 2019, A Model Decomposition Framework for LPV Systems. in 2018 IEEE Conference on Decision and Control, CDC 2018., 8618676, Proceedings of the IEEE Conference on Decision and Control, vol. 2018-December, Institute of Electrical and Electronics Engineers Inc., pp. 5898-5903, 57th IEEE Conference on Decision and Control, CDC 2018, Miami, United States, 12/17/18. https://doi.org/10.1109/CDC.2018.8618676
Luspay T, Peni T, Seiler Jr PJ, Vanek B. A Model Decomposition Framework for LPV Systems. In 2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 5898-5903. 8618676. (Proceedings of the IEEE Conference on Decision and Control). https://doi.org/10.1109/CDC.2018.8618676
Luspay, Tamas ; Peni, Tamas ; Seiler Jr, Peter J ; Vanek, Balint. / A Model Decomposition Framework for LPV Systems. 2018 IEEE Conference on Decision and Control, CDC 2018. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 5898-5903 (Proceedings of the IEEE Conference on Decision and Control).
@inproceedings{6677c9dcfb3744a8bbcc188fa15bfe58,
title = "A Model Decomposition Framework for LPV Systems",
abstract = "The paper proposes a systematic framework for efficient decomposition of Linear Parameter Varying (LPV) systems. Our aim is to reveal the topological structure of the system, to facilitate various analysis and synthesis methods. For this purpose, first we extend the notion of Gramian based interaction measure for parameter dependent systems. However, the metric is based on the solution of an iterative optimization, subject to Linear Matrix Inequality (LMI) constraints. Therefore, in order to ease the computation burden, we apply a modal decomposition to the system. A simple structured Gramian computation is introduced, with fast conic programming. The proposed methodology is illustrated by a numerical example.",
author = "Tamas Luspay and Tamas Peni and {Seiler Jr}, {Peter J} and Balint Vanek",
year = "2019",
month = "1",
day = "18",
doi = "10.1109/CDC.2018.8618676",
language = "English (US)",
series = "Proceedings of the IEEE Conference on Decision and Control",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5898--5903",
booktitle = "2018 IEEE Conference on Decision and Control, CDC 2018",

}

TY - GEN

T1 - A Model Decomposition Framework for LPV Systems

AU - Luspay, Tamas

AU - Peni, Tamas

AU - Seiler Jr, Peter J

AU - Vanek, Balint

PY - 2019/1/18

Y1 - 2019/1/18

N2 - The paper proposes a systematic framework for efficient decomposition of Linear Parameter Varying (LPV) systems. Our aim is to reveal the topological structure of the system, to facilitate various analysis and synthesis methods. For this purpose, first we extend the notion of Gramian based interaction measure for parameter dependent systems. However, the metric is based on the solution of an iterative optimization, subject to Linear Matrix Inequality (LMI) constraints. Therefore, in order to ease the computation burden, we apply a modal decomposition to the system. A simple structured Gramian computation is introduced, with fast conic programming. The proposed methodology is illustrated by a numerical example.

AB - The paper proposes a systematic framework for efficient decomposition of Linear Parameter Varying (LPV) systems. Our aim is to reveal the topological structure of the system, to facilitate various analysis and synthesis methods. For this purpose, first we extend the notion of Gramian based interaction measure for parameter dependent systems. However, the metric is based on the solution of an iterative optimization, subject to Linear Matrix Inequality (LMI) constraints. Therefore, in order to ease the computation burden, we apply a modal decomposition to the system. A simple structured Gramian computation is introduced, with fast conic programming. The proposed methodology is illustrated by a numerical example.

UR - http://www.scopus.com/inward/record.url?scp=85062181742&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062181742&partnerID=8YFLogxK

U2 - 10.1109/CDC.2018.8618676

DO - 10.1109/CDC.2018.8618676

M3 - Conference contribution

T3 - Proceedings of the IEEE Conference on Decision and Control

SP - 5898

EP - 5903

BT - 2018 IEEE Conference on Decision and Control, CDC 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -