A Model Decomposition Framework for LPV Systems

Tamas Luspay, Tamas Peni, Peter Seiler, 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

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.

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