z-logo
open-access-imgOpen Access
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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here
Accelerating Research

Address

John Eccles House
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom