Wu, Zhe, Christofides, Panagiotis D., Wu, Wanlu, Wang, Yujia, Abdullah, Fahim, Alnajdi, Aisha and Kadakia, Yash. "A tutorial review of machine learning-based model predictive control methods" Reviews in Chemical Engineering, vol. 41, no. 4, 2025, pp. 359-400. https://doi.org/10.1515/revce-2024-0055
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.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research / project is supported by the National Research Foundation, Singapore - Competitive Research Programme
Grant Reference no. : 27-2021-0001
This research / project is supported by the Ministry of Education, Singapore - Academic Research Fund Tier 1
Grant Reference no. : 22-5367-A0001
This research / project is supported by the Agency for Science, Technology and Research (A*STAR) - Manufacturing, Trade, and Connectivity Young Individual Research Grant
Grant Reference no. : M22K3c0093