This repository contains the source code used to produce the results obtained in Learning-Based Model Predictive Control for Piecewise Affine Systems with Feasibility Guarantees published in ECC 2025.
In this work we propose a learning-based model predictive controller for piecewise affine systems.
If you find the paper or this repository helpful in your publications, please consider citing it.
@inproceedings{mallick2025learning,
title={Learning-based model predictive control for piecewise affine systems with feasibility guarantees},
author={Mallick, Samuel and Dabiri, Azita and De Schutter, Bart},
booktitle={2025 European Control Conference (ECC)},
pages={345--350},
year={2025},
organization={IEEE}
}The code was created with Python 3.12. To access it, clone the repository
git clone https://github.com/SamuelMallick/supervised-learning-pwa-mpc
cd supervised-learning-pwa-mpcand then install the required packages by, e.g., running
pip install -r requirements.txt- The scripts used to generate the results in the paper are found in
examples/paper_2024. - The core code for the approach is in the package source code
src.
The repository is provided under the GNU General Public License. See the LICENSE file included with this repository.
Samuel Mallick, PhD Candidate [s.mallick@tudelft.nl | sam.mallick.97@gmail.com]
Delft Center for Systems and Control in Delft University of Technology
This research is part of a project that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant agreement No. 101018826 - CLariNet).
Copyright (c) 2025 Samuel Mallick.
Copyright notice: Technische Universiteit Delft hereby disclaims all copyright interest in the program “mpcrl-vehicle-gearse” (Learning-Based Model Predictive Control for Piecewise Affine Systems with Feasibility Guarantees) written by the Author(s). Prof. Dr. Ir. Fred van Keulen, Dean of 3mE.