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.
Publisher page
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BibTeX
@inproceedings{GhaDeS:24-014,
author = {Gharavi, Leila and De Schutter, Bart and Baldi, Simone},
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}
}