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

@article{MalBat:25-016,
   author  = {Mallick, Samuel and Battocletti, Gianpietro and Dong, Qizhang and
              Dabiri, Azita and De Schutter, Bart},
   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}
   }

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