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Stochastic model predictive control with driver behavior learning for improved powertrain control

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)

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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.

Item Type: Book Section
Identification Number: 10.1109/CDC.2010.5717791
Additional Information: Proceeding of the 49th IEEE Conference on Decision and Control, Hilton Atlanta Hotel, Atlanta, December 15-17, 2010
Uncontrolled 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
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Research Area: Computer Science and Applications
Depositing User: Professor Alberto Bemporad
Date Deposited: 29 Jul 2011 10:52
Last Modified: 17 Nov 2011 11:01
URI: http://eprints.imtlucca.it/id/eprint/738

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