Learning-based MPC for fuel efficient control of autonomous vehicles with discrete gear selection


Reference:

S. Mallick, G. Battocletti, Q. Dong, A. Dabiri, and B. De Schutter, "Learning-based MPC for fuel efficient control of autonomous vehicles with discrete gear selection," IEEE Control Systems Letters, vol. 9, pp. 1117-1122, 2025.

Abstract:

Co-optimization of both vehicle speed and gear position via model predictive control (MPC) has been shown to offer benefits for fuel-efficient autonomous driving. However, optimizing both the vehicle’s continuous dynamics and discrete gear positions may be too computationally intensive for a real-time implementation. This letter proposes a learning-based MPC scheme to address this issue. A policy is trained to select and fix the gear positions across the prediction horizon of the MPC controller, leaving a significantly simpler continuous optimization problem to be solved online. In simulation, the proposed approach is shown to have a significantly lower computation burden and a comparable performance, with respect to pure MPC-based co-optimization.

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Bibtex entry:

@article{MalBat:25-016,
author={S. Mallick and G. Battocletti and Q. Dong and A. Dabiri and B. {D}e Schutter},
title={Learning-based {MPC} for fuel efficient control of autonomous vehicles with discrete gear selection},
journal={IEEE Control Systems Letters},
volume={9},
pages={1117--1122},
year={2025},
doi={10.1109/LCSYS.2025.3575335}
}


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