Leveraging Reinforcement Learning and Koopman Theory for Enhanced Model Predictive Control Performance

TL;DR

This paper introduces a novel control framework combining Koopman theory and Deep Reinforcement Learning to improve Model Predictive Control, demonstrating superior stability, constraint satisfaction, and cost efficiency in case studies.

Contribution

It presents an innovative end-to-end learning approach that integrates Koopman-based models with DRL for enhanced MPC performance, a novel combination in control strategies.

Findings

Outperforms traditional controllers in stability and cost savings

Achieves higher constraint satisfaction in case studies

Demonstrates robustness in complex control tasks

Abstract

This study presents an innovative approach to Model Predictive Control (MPC) by leveraging the powerful combination of Koopman theory and Deep Reinforcement Learning (DRL). By transforming nonlinear dynamical systems into a higher-dimensional linear regime, the Koopman operator facilitates the linear treatment of nonlinear behaviors, paving the way for more efficient control strategies. Our methodology harnesses the predictive prowess of Koopman-based models alongside the optimization capabilities of DRL, particularly using the Proximal Policy Optimization (PPO) algorithm, to enhance the controller's performance. The resulting end-to-end learning framework refines the predictive control policies to cater to specific operational tasks, optimizing both performance and economic efficiency. We validate our approach through rigorous NMPC and eNMPC case studies, demonstrating that the Koopman-RL controller outperforms traditional controllers by achieving higher stability, superior constraint satisfaction, and significant cost savings. The findings indicate that our model can be a robust tool for complex control tasks, offering valuable insights into future applications of RL in MPC.