@inproceedings{9f857f06e5294b4ab4eac4ea1ced9666,
title = "Improving Nonlinear Model Predictive Control Laws via Implicit Q-Learning",
abstract = "This paper presents an implicit Q-Learning scheme to improve the performance of nonlinear model predictive control laws while providing a stability guarantee. The control space of this learning-based method is restricted to the admissible control set of a Lyapunov-based nonlinear model predictive controller. The effectiveness of this method is demonstrated on a highly nonlinear chemical process system with practical significance. It is shown that learning-based controller derived with this method improves the performance of a sub-optimal baseline controller beyond what is possible by supervised learning approximation approaches. This scheme offers a promising new paradigm for improving model-based controllers that deteriorate due to the dynamic process changes typically encountered in real-world systems.",
keywords = "chemical reactions, deep learning, Model predictive control, process control, reinforcement learning",
author = "Khalid Alhazmi and \{Mani Sarathy\}, S.",
note = "Publisher Copyright: Copyright {\textcopyright} 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/); 22nd IFAC World Congress ; Conference date: 09-07-2023 Through 14-07-2023",
year = "2023",
month = jul,
day = "1",
doi = "10.1016/j.ifacol.2023.10.869",
language = "English (US)",
series = "IFAC-PapersOnLine",
publisher = "Elsevier B.V.",
number = "2",
pages = "10027--10032",
editor = "Hideaki Ishii and Yoshio Ebihara and Jun-ichi Imura and Masaki Yamakita",
booktitle = "IFAC-PapersOnLine",
edition = "2",
}