eprintid: 1653
rev_number: 9
eprint_status: archive
userid: 6
dir: disk0/00/00/16/53
datestamp: 2013-08-05 08:53:53
lastmod: 2014-07-01 12:39:39
status_changed: 2013-08-05 08:53:53
type: article
metadata_visibility: show
creators_name: Di Cairano, Stefano
creators_name: Bernardini, Daniele
creators_name: Bemporad, Alberto
creators_name: Kolmanovsky, Ilya
creators_id: 
creators_id: daniele.bernardini@imtlucca.it
creators_id: alberto.bemporad@imtlucca.it
creators_id: 
title: Stochastic MPC with learning for driver-predictive vehicle control and its application to HEV energy management
ispublished: pub
subjects: QA75
subjects: TL
divisions: CSA
full_text_status: none
keywords: Automotive controls, driver-machine interaction, energy management, model predictive control (MPC), optimization, real-time learning, stochastic control
abstract: This paper develops an approach for driver-aware vehicle control based on stochastic model predictive control with learning (SMPCL). The framework combines the on-board learning of a Markov chain that represents the driver behavior, a scenario-based approach for stochastic optimization, and quadratic programming. By using quadratic programming, SMPCL can handle, in general, larger state dimension models than stochastic dynamic programming, and can reconfigure in real-time for accommodating changes in driver behavior. The SMPCL approach is demonstrated in the energy management of a series hybrid electrical vehicle, aimed at improving fuel efficiency while enforcing constraints on battery state of charge and power. The SMPCL controller allocates the power from the battery and the engine to meet the driver power request. A Markov chain that models the power request dynamics is learned in real-time to improve the prediction capabilities of model predictive control (MPC). Because of exploiting the learned pattern of the driver behavior, the proposed approach outperforms conventional model predictive control and shows performance close to MPC with full knowledge of future driver power request in standard and real-world driving cycles.
date: 2014
date_type: published
publication: IEEE Transactions on Control Systems Technology 
volume: 22
number: 3
publisher: IEEE
pagerange: 1018-1031
id_number: 10.1109/TCST.2013.2272179
refereed: TRUE
issn: 1063-6536
official_url: http://dx.doi.org/10.1109/TCST.2013.2272179
citation:   Di Cairano, Stefano and Bernardini, Daniele and Bemporad, Alberto and Kolmanovsky, Ilya  Stochastic MPC with learning for driver-predictive vehicle control and its application to HEV energy management.  IEEE Transactions on Control Systems Technology , 22 (3).  pp. 1018-1031.  ISSN 1063-6536      (2014)