This paper presents a comprehensive overview of data-driven model predictive control, highlighting state-of-the-art methodologies and their numerical implementation. The discussion begins with a brief review of conventional model predictive control (MPC), which discusses both linear MPC (LMPC) and nonlinear MPC (NMPC). This is followed by a section on data-driven LMPC, outlining fundamental concepts and the implementation of various approaches, including subspace predictive control and prediction error methods. Subsequently, the focus shifts to data-driven NMPC, emphasizing approaches based on neural network models. The paper concludes with a review of recent advancements in data-driven MPC and explores potential directions for future research.
@article{arxiv.2505.11524,
title = {Data-driven Model Predictive Control using MATLAB},
author = {Midhun T. Augustine},
journal= {arXiv preprint arXiv:2505.11524},
year = {2025}
}