eprintid: 2459
rev_number: 10
eprint_status: archive
userid: 6
dir: disk0/00/00/24/59
datestamp: 2015-01-12 12:41:33
lastmod: 2015-01-12 12:42:48
status_changed: 2015-01-12 12:41:33
type: book_section
metadata_visibility: show
creators_name: Formentin, Simone
creators_name: Piga, Dario
creators_name: Tóth, Roland
creators_name: Savaresi, Sergio M.
creators_id: 
creators_id: dario.piga@imtlucca.it
creators_id: 
creators_id: 
title: Direct data-driven control of linear parameter-varying systems
ispublished: pub
subjects: QA75
divisions: CSA
full_text_status: public
keywords: Data models; Dynamic scheduling; Instruments; Noise measurement; Optimization; Trajectory; Tuning

abstract: In many control applications, nonlinear plants can be modeled as linear parameter-varying (LPV) systems, by which the dynamic behavior is assumed to be linear, but also dependent on some measurable signals, e.g., operating conditions. When a measured data set is available, LPV model identification can provide low complexity linear models that can embed the underlying nonlinear dynamic behavior of the plant. For such models, powerful control synthesis tools are available, but the way the modeling error and the conservativeness of the embedding affect the control performance is still largely unknown. Therefore, it appears to be attractive to directly synthesize the controller from data without modeling the plant. In this paper, a novel data-driven synthesis scheme is proposed to lay the basic foundations of future research on this challenging problem. The effectiveness of the proposed approach is illustrated by a numerical example.
date: 2013-12
publisher: IEEE
pagerange: 4110-4115
event_title: Decision and Control (CDC), 2013 IEEE 52nd Annual Conference on
id_number: 10.1109/CDC.2013.6760519
refereed: TRUE
isbn: 978-1-4673-5714-2 
book_title: Proceedings of the 52nd Annual Conference on Decision and Control (CDC), 2013
official_url: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6760519&isnumber=6759837
funders: This work was partially supported by the Netherlands Organization for Scientific Research (NWO, grant. no.: 639.021.127).
citation:   Formentin, Simone and Piga, Dario and Tóth, Roland and Savaresi, Sergio M.  Direct data-driven control of linear parameter-varying systems.   In:  Proceedings of the 52nd Annual Conference on Decision and Control (CDC), 2013.   IEEE, pp. 4110-4115.  ISBN 978-1-4673-5714-2       (2013)  
document_url: http://eprints.imtlucca.it/2459/1/CDC2013Piga_preprint.pdf