3 papers
cs.LG2025
Optimizing Operation Recipes with Reinforcement Learning for Safe and Interpretable Control of Chemical Processes
Dean Brandner, Sergio Lucia
Optimal operation of chemical processes is vital for energy, resource, and cost savings in chemical engineering. The problem of optimal operation can be tackled with reinforcement…
eess.SY2025
Computationally efficient Gauss-Newton reinforcement learning for model predictive control
Dean Brandner, Sebastien Gros, Sergio Lucia
Model predictive control (MPC) is widely used in process control due to its interpretability and ability to handle constraints. As a parametric policy in reinforcement learning (RL…
cs.LG2024
Reinforced Model Predictive Control via Trust-Region Quasi-Newton Policy Optimization
Dean Brandner, Sergio Lucia
Model predictive control can optimally deal with nonlinear systems under consideration of constraints. The control performance depends on the model accuracy and the prediction hori…