Learning-Based MPC for Fuel Efficient Control of Autonomous Vehicles With Discrete Gear Selection
S.H. Mallick (TU Delft - Team Bart De Schutter)
G. Battocletti (TU Delft - Team Bart De Schutter)
Qizhang Dong (Student TU Delft)
A. Dabiri (TU Delft - Team Azita Dabiri)
B. De Schutter (TU Delft - Delft Center for Systems and Control)
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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 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.
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