A Model Decomposition Framework for LPV Systems

Tamas Luspay; Tamas Peni; Peter Seiler; Balint Vanek

doi:10.1109/CDC.2018.8618676

A Model Decomposition Framework for LPV Systems

Tamas Luspay, Tamas Peni, Peter Seiler, Balint Vanek

Aerospace Engineering and Mechanics

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	2018 IEEE Conference on Decision and Control, CDC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	5898-5903
Number of pages	6
ISBN (Electronic)	9781538613955
DOIs	https://doi.org/10.1109/CDC.2018.8618676
State	Published - Jul 2 2018
Event	57th IEEE Conference on Decision and Control, CDC 2018 - Miami, United States Duration: Dec 17 2018 → Dec 19 2018

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
Volume	2018-December
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Conference

Conference	57th IEEE Conference on Decision and Control, CDC 2018
Country/Territory	United States
City	Miami
Period	12/17/18 → 12/19/18

Bibliographical note

Funding Information:
ACKNOWLEDGEMENT The research leading to these results is part of the FLEXOP project. This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 636307. This paper was supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences. The research reported in this paper was supported by the Higher Education Excellence Program of the Ministry of Human Capacities in the frame of Artificial Intelligence research area of Budapest University of Technology and Economics (BME FIKPMI/FM).

Funding Information:
The research leading to these results is part of the FLEXOP project. This project has received funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 636307. This paper was supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences.

Publisher Copyright:
© 2018 IEEE.

Publisher link

10.1109/CDC.2018.8618676

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A Model Decomposition Framework for LPV Systems. / Luspay, Tamas; Peni, Tamas; Seiler, Peter et al.

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Luspay, T, Peni, T, Seiler, P & Vanek, B 2018, 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

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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.",

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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.

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A Model Decomposition Framework for LPV Systems

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