eprintid: 1471
rev_number: 7
eprint_status: archive
userid: 6
dir: disk0/00/00/14/71
datestamp: 2013-02-12 12:11:05
lastmod: 2013-02-12 12:11:05
status_changed: 2013-02-12 12:11:05
type: book_section
metadata_visibility: show
creators_name: Rubagotti, Matteo
creators_name: Poggi, Tomaso
creators_name: Bemporad, Alberto
creators_name: Storace, Marco
creators_id: 
creators_id: 
creators_id: alberto.bemporad@imtlucca.it
creators_id: 
title: Piecewise affine direct virtual sensors with Reduced Complexity
ispublished: pub
subjects: QA75
subjects: TJ
divisions: CSA
full_text_status: none
keywords: Complexity theory; Estimation; Observability; Sensor systems; Vectors; Voltage control.
note: 51st IEEE Conference on Decision and Control December 10-13, 2012. Maui, Hawaii, USA
abstract: In this paper, a piecewise-affine direct virtual sensor is proposed for the estimation of unmeasured outputs of nonlinear systems whose dynamical model is unknown. In order to overcome the lack of a model, the virtual sensor is designed directly from measured inputs and outputs. The proposed approach generalizes a previous contribution, allowing one to design lower-complexity estimators. Indeed, the reduced-complexity approach strongly reduces the effect of the so-called "curse of dimensionality", and can be applied to relatively high-order systems, while enjoying all the convergence and optimality properties of the original approach.
date: 2012-12
date_type: published
publisher: IEEE
pagerange: 656 -661
event_title: Decision and Control (CDC), 2012 IEEE 51st Annual Conference on
id_number: 10.1109/CDC.2012.6426755
refereed: TRUE
isbn: 978-1-4673-2064-1
book_title: Proceedings of the IEEE 51st Annual Conference on Decision and Control (CDC), 2012
official_url: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6426755&isnumber=6425800
citation:   Rubagotti, Matteo and Poggi, Tomaso and Bemporad, Alberto and Storace, Marco  Piecewise affine direct virtual sensors with Reduced Complexity.   In:  Proceedings of the IEEE 51st Annual Conference on Decision and Control (CDC), 2012.   IEEE, 656 -661.  ISBN 978-1-4673-2064-1      (2012)