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Stochastic MPC with learning for driver-predictive vehicle control and its application to HEV energy management

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)

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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.

Item Type: Article
Identification Number: 10.1109/TCST.2013.2272179
Uncontrolled Keywords: Automotive controls, driver-machine interaction, energy management, model predictive control (MPC), optimization, real-time learning, stochastic control
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Research Area: Computer Science and Applications
Depositing User: Ms T. Iannizzi
Date Deposited: 05 Aug 2013 08:53
Last Modified: 01 Jul 2014 12:39
URI: http://eprints.imtlucca.it/id/eprint/1653

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