A tutorial review of machine learning-based model predictive control methods
Zhe Wu, Panagiotis D. Christofides, Wanlu Wu, Yujia Wang, Fahim Abdullah, Aisha Alnajdi, Yash A. Kadakia
Abstract
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.