TY  - JOUR
Y1  - 2014///
JF  - IEEE Transactions on Control Systems Technology 
IS  - 3
A1  - Di Cairano, Stefano
A1  - Bernardini, Daniele
A1  - Bemporad, Alberto
A1  - Kolmanovsky, Ilya
PB  - IEEE
SP  - 1018
VL  - 22
TI  - Stochastic MPC with learning for driver-predictive vehicle control and its application to HEV energy management
AV  - none
KW  - Automotive controls
KW  -  driver-machine interaction
KW  -  energy management
KW  -  model predictive control (MPC)
KW  -  optimization
KW  -  real-time learning
KW  -  stochastic control
UR  - http://dx.doi.org/10.1109/TCST.2013.2272179
SN  - 1063-6536
N2  - 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.
ID  - eprints1653
EP  - 1031
ER  -