eprintid: 3543
rev_number: 5
eprint_status: archive
userid: 69
dir: disk0/00/00/35/43
datestamp: 2016-10-04 08:36:36
lastmod: 2016-10-04 08:36:36
status_changed: 2016-10-04 08:36:36
type: article
metadata_visibility: show
creators_name: Gros, Sébastien
creators_name: Zanon, Mario
creators_name: Quirynen, Rien
creators_name: Bemporad, Alberto
creators_name: Diehl, Moritz
creators_id: 
creators_id: 
creators_id: 
creators_id: alberto.bemporad@imtlucca.it
creators_id: 
title: From linear to nonlinear MPC: bridging the gap via the real-time iteration
ispublished: inpress
subjects: QA75
divisions: CSA
full_text_status: none
abstract: Linear model predictive control (MPC) can be currently deployed at outstanding speeds, thanks to recent progress in algorithms for solving online the underlying structured quadratic programs. In contrast, nonlinear MPC (NMPC) requires the deployment of more elaborate algorithms, which require longer computation times than linear MPC. Nonetheless, computational speeds for NMPC comparable to those of MPC are now regularly reported, provided that the adequate algorithms are used. In this paper, we aim at clarifying the similarities and differences between linear MPC and NMPC. In particular, we focus our analysis on NMPC based on the real-time iteration (RTI) scheme, as this technique has been successfully tested and, in some applications, requires computational times that are only marginally larger than linear MPC. The goal of the paper is to promote the understanding of RTI-based NMPC within the linear MPC community.
date: 2016
date_type: published
publication: International Journal of Control
volume: 0
number: 0
publisher: Taylor & Francis
pagerange: 1-19
id_number: 10.1080/00207179.2016.1222553
refereed: TRUE
issn: 0020-7179
official_url: http://dx.doi.org/10.1080/00207179.2016.1222553
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citation:   Gros, Sébastien and Zanon, Mario and Quirynen, Rien and Bemporad, Alberto and Diehl, Moritz  From linear to nonlinear MPC: bridging the gap via the real-time iteration.  International Journal of Control.  pp. 1-19.  ISSN 0020-7179    (In Press)   (2016)