2025/05/12 by Md Nur-A-Adam Dony, Dony, Md Nur-A-Adam · 1 citation
Computer Science · Physics and Astronomy · #68Q32 (Computational Learning Theory) #68T05 (Learning and Adaptive Systems) #93A30 (Mathematical Systems Theory) #Adaptive Dynamic Programming Control #FOS: Electrical engineering #Model Reduction and Neural Networks #Reinforcement Learning in Robotics #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2505.08122
openalex publication_date 2025/05/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.