eprintid: 738
rev_number: 23
eprint_status: archive
userid: 7
dir: disk0/00/00/07/38
datestamp: 2011-07-29 10:52:40
lastmod: 2011-11-17 11:01:57
status_changed: 2011-07-29 10:52:40
type: book_section
metadata_visibility: show
item_issues_count: 0
creators_name: Bichi, M.
creators_name: Ripaccioli, Giulio
creators_name: Di Cairano, Stefano
creators_name: Bernardini, Daniele
creators_name: Bemporad, Alberto
creators_name: Kolmanovsky, Ilya
creators_id: 
creators_id: 
creators_id: 
creators_id: daniele.bernardini@imtlucca.it
creators_id: alberto.bemporad@imtlucca.it
creators_id: 
title: Stochastic model predictive control with driver behavior learning for improved powertrain control
ispublished: pub
subjects: QA
subjects: QA75
subjects: TL
divisions: CSA
full_text_status: none
keywords: adaptive cruise control; driver behavior learning; energy management; powertrain control; series hybrid electric vehicle; stochastic model predictive control; vehicle dynamics; adaptive control; hybrid electric vehicles; learning (artificial intelligence); predictive control; stochastic systems
note: Proceeding of the 49th IEEE Conference on Decision and Control, Hilton Atlanta Hotel, Atlanta, December 15-17, 2010
abstract: In this paper we advocate the use of stochastic model predictive control (SMPC) for improving the performance of powertrain control algorithms, by optimally controlling the complex system composed of driver and vehicle. While the powertrain is modeled as the deterministic component of the dynamics, the driver behavior is represented as a 
stochastic system which affects the vehicle dynamics. Since stochastic MPC is based on online numerical optimization, the driver model can be learned online, hence allowing the control algorithm to adapt to different drivers and drivers' behaviors. The proposed technique is evaluated in two applications: adaptive cruise control, where the driver behavioral model is used to predict the leading vehicle dynamics, and series hybrid electric vehicle (SHEV) energy management, where the driver model is used to predict the future power requests.
date: 2010-12
date_type: published
publisher: IEEE
pagerange: 6077 -6082
event_title: Decision and Control (CDC), 2010 49th IEEE Conference on
id_number: 10.1109/CDC.2010.5717791
refereed: TRUE
isbn: 978-1-4244-7745-6 
book_title: 49th IEEE Conference on Decision and Control
official_url: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5717791&isnumber=5716927
citation:   Bichi, M. and Ripaccioli, Giulio and Di Cairano, Stefano and Bernardini, Daniele and Bemporad, Alberto and Kolmanovsky, Ilya  Stochastic model predictive control with driver behavior learning for improved powertrain control.   In:  49th IEEE Conference on Decision and Control.   IEEE, 6077 -6082.  ISBN 978-1-4244-7745-6       (2010)