relation: http://eprints.imtlucca.it/2459/
title: Direct data-driven control of linear parameter-varying systems
creator: Formentin, Simone
creator: Piga, Dario
creator: Tóth, Roland
creator: Savaresi, Sergio M.
subject: QA75 Electronic computers. Computer science
description: 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.
publisher: IEEE
date: 2013-12
type: Book Section
type: PeerReviewed
format: application/pdf
language: en
identifier: http://eprints.imtlucca.it/2459/1/CDC2013Piga_preprint.pdf
identifier:   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)  
relation: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6760519&isnumber=6759837
relation: 10.1109/CDC.2013.6760519