eprintid: 2475
rev_number: 5
eprint_status: archive
userid: 6
dir: disk0/00/00/24/75
datestamp: 2015-01-13 14:22:23
lastmod: 2015-01-13 14:22:23
status_changed: 2015-01-13 14:22:23
type: article
metadata_visibility: show
creators_name: Canale, Massimo
creators_name: Cerone, Vito
creators_name: Piga, Dario
creators_name: Regruto, Diego
creators_id: 
creators_id: 
creators_id: dario.piga@imtlucca.it
creators_id: 
title: Approximation of model predictive control laws for polynomial systems
ispublished: pub
subjects: QA75
divisions: CSA
full_text_status: none
keywords: LMI relaxation, polynomial optimization, model predictive control
abstract: A fast implementation of a given predictive controller for polynomial systems is introduced by approximating the optimal control law with a piecewise constant function defined over a hyper-cube partition of the system state space. Such a state-space partition is computed in order to guarantee stability, an a priori fixed trajectory error as well as input and state constraints fulfilment. The presented approximation procedure is achieved by solving a set of nonconvex polynomial optimization problems, whose approximate solutions are computed by means of semidefinite relaxation techniques for semialgebraic problems.
date: 2014-09
date_type: published
publication: Asian Journal of Control 
volume: 16
number: 5
publisher: Wiley-Blackwell
pagerange: 1425-1436
id_number: 10.1002/asjc.863
refereed: TRUE
issn: 1561-8625
official_url: http://dx.doi.org/10.1002/asjc.863
citation:   Canale, Massimo and Cerone, Vito and Piga, Dario and Regruto, Diego  Approximation of model predictive control laws for polynomial systems.  Asian Journal of Control , 16 (5).  pp. 1425-1436.  ISSN 1561-8625      (2014)