Improving Nonlinear Model Predictive Control Laws via Implicit Q-Learning
Year: 2023
Type: article
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.
Source: IFAC-PapersOnLine
Authors: Khalid Alhazmi, S. Mani Sarathy
Institution: King Abdullah University of Science and Technology
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4388918092