H4MPC: A Hybridization Toolbox for Model Predictive Control in Automated Driving

Reference

L. Gharavi, B. De Schutter, and S. Baldi, "H4MPC: A Hybridization Toolbox for Model Predictive Control in Automated Driving," Proceedings of the 2024 IEEE 18th International Conference on Advanced Motion Control (AMC2024), Kyoto, Japan, 6 pp., Feb.-Mar. 2024.

Abstract

The computational complexity of nonlinear Model Predictive Control (MPC) poses a significant challenge in achieving real-time levels of 4 and 5 of automated driving. This work presents the open-access Hybridization toolbox for MPC (H4MPC), targeting computational efficiency of nonlinear MPC thanks to several modules to hybridize nonlinear MPC optimization problems commonly encountered in automated driving applications. H4MPC is designed as a user-friendly solution with a graphical user interface within the MATLAB environment. The toolbox facilitates intuitive and straightforward customization of the hybridization process for any given function appearing in the equality or inequality constraints within the MPC framework. The initial release, Version 1.0, is freely available from https://bit.ly/H4MPCV1. To provide a clear illustration of the toolbox capabilities, we present two case studies: one to hybridize a vehicle model and another one to approximate tire saturation constraints.

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Bibtex entry

@inproceedings{GhaDeS:24-014,
author={L. Gharavi and B. {D}e Schutter and S. Baldi},
title={{H4MPC}: {A} Hybridization Toolbox for Model Predictive Control in Automated Driving},
booktitle={Proceedings of the 2024 IEEE 18th International Conference on Advanced Motion Control (AMC2024)},
address={Kyoto, Japan},
month=feb # {--} # mar,
year={2024},
doi={10.1109/AMC58169.2024.10505665}
}

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