A tutorial review of machine learning-based model predictive control methods

Wu, Zhe; Christofides, Panagiotis D.; Wu, Wanlu; Wang, Yujia; Abdullah, Fahim; Alnajdi, Aisha; Kadakia, Yash

doi:10.1515/revce-2024-0055

Title: A tutorial review of machine learning-based model predictive control methods

Journal Article · 09 December 2024 · Reviews in Chemical Engineering

DOI: https://doi.org/10.1515/revce-2024-0055 · OSTI ID:2480589

^[1]; Christofides, Panagiotis D. ^[2]; Wu, Wanlu ^[1]; Wang, Yujia ^[1]; Abdullah, Fahim ^[3]; Alnajdi, Aisha ^[3]; Kadakia, Yash ^[3]

Department of Chemical and Biomolecular Engineering , 37580 National University of Singapore , Singapore , Singapore
Department of Chemical and Biomolecular Engineering, Department of Electrical and Computer Engineering , University of California , Los Angeles , CA , USA
Department of Chemical and Biomolecular Engineering , University of California , Los Angeles , CA , USA

Abstract This tutorial review provides a comprehensive overview of machine learning (ML)-based model predictive control (MPC) methods, covering both theoretical and practical aspects. It provides a theoretical analysis of closed-loop stability based on the generalization error of ML models and addresses practical challenges such as data scarcity, data quality, the curse of dimensionality, model uncertainty, computational efficiency, and safety from both modeling and control perspectives. The application of these methods is demonstrated using a nonlinear chemical process example, with open-source code available on GitHub. The paper concludes with a discussion on future research directions in ML-based MPC.

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Sponsoring Organization:: USDOE

OSTI ID:: 2480589

Journal Information:: Reviews in Chemical Engineering, Journal Name: Reviews in Chemical Engineering Journal Issue: 4 Vol. 41; ISSN 0167-8299

Publisher:: Walter de Gruyter GmbHCopyright Statement

Country of Publication:: Germany

Language:: English

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