eprintid: 429
rev_number: 12
eprint_status: archive
userid: 7
dir: disk0/00/00/04/29
datestamp: 2011-07-27 08:31:59
lastmod: 2011-11-17 11:28:12
status_changed: 2011-07-27 08:31:59
type: book_section
metadata_visibility: show
contact_email: alberto.bemporad@imtlucca.it
item_issues_count: 0
creators_name: Bernardini, Daniele
creators_name: Bemporad, Alberto
creators_id: daniele.bernardini@imtlucca.it
creators_id: alberto.bemporad@imtlucca.it
title: Scenario-based model predictive control of stochastic constrained linear systems
ispublished: pub
subjects: QA
subjects: TA
divisions: CSA
full_text_status: none
keywords: Control systems; Convergence; Linear matrix inequalities; Linear systems; Predictive control; Predictive models; Robust control; Stability; Stochastic processes; Stochastic systems 
note: Proceeding of the 48th IEEE Conference on Decision and Control, Shanghai, China
abstract: In this paper we propose a stochastic model predictive control (MPC) formulation based on scenario generation for linear systems affected by discrete multiplicative disturbances. By separating the problems of (1) stochastic performance, and (2) stochastic stabilization and robust constraints fulfillment of the closed-loop system, we aim at obtaining a less conservative control action with respect to classical robust MPC schemes, still enforcing convergence and feasibility properties for the controlled system. Stochastic performance is addressed for very general classes of stochastic disturbance processes, although discretized in the probability space, by adopting ideas from multi-stage stochastic optimization. Stochastic stability and recursive feasibility are enforced through linear matrix inequality (LMI) problems, which are solved off-line; stochastic performance is optimized by an on-line MPC problem which is formulated as a convex quadratically constrained quadratic program (QCQP) and solved in a receding horizon fashion. The performance achieved by the proposed approach is shown in simulation and compared to the one obtained by standard robust and deterministic MPC schemes.
date: 2009
date_type: published
publication: Decision and Control
publisher: IEEE
pagerange: 6333-6338
event_title: 48th  IEEE Conf. on Decision and Control
event_location: 16th Dec. - 18th Dec 2009
event_dates: Shanghai, China
id_number: 10.1109/CDC.2009.5399917  
refereed: TRUE
isbn: 978-1-4244-3871-6 
book_title: 48th IEEE Conference on Decision and Control
official_url: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5399917&tag=1
citation:   Bernardini, Daniele and Bemporad, Alberto  Scenario-based model predictive control of stochastic constrained linear systems.   In:  48th IEEE Conference on Decision and Control.   IEEE, pp. 6333-6338.  ISBN 978-1-4244-3871-6       (2009)