
Learning-Based MPC for Fuel Efficient Control of Autonomous Vehicles With Discrete Gear Selection
Author(s) -
Samuel Mallick,
Gianpietro Battocletti,
Qizhang Dong,
Azita Dabiri,
Bart De Schutter
Publication year - 2025
Publication title -
ieee control systems letters
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 1.154
H-Index - 21
eISSN - 2475-1456
DOI - 10.1109/lcsys.2025.3575335
Subject(s) - robotics and control systems , computing and processing , components, circuits, devices and systems
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 work 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.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom