TY  - CHAP
T2  - 49th IEEE Conference on Decision and Control
EP  - 6082
N1  - Proceeding of the 49th IEEE Conference on Decision and Control, Hilton Atlanta Hotel, Atlanta, December 15-17, 2010
ID  - eprints738
N2  - 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.
SP  - 6077 
A1  - Bichi, M.
A1  - Ripaccioli, Giulio
A1  - Di Cairano, Stefano
A1  - Bernardini, Daniele
A1  - Bemporad, Alberto
A1  - Kolmanovsky, Ilya
PB  - IEEE
SN  - 978-1-4244-7745-6 
UR  - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=5717791&isnumber=5716927
AV  - none
TI  - Stochastic model predictive control with driver behavior learning for improved powertrain control
Y1  - 2010/12//
KW  - 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
ER  -