14 citations · 20 across the 8 of their papers we have counts for
7 papers · 1 filter
Data-driven Koopman MPC using Mixed Stochastic-Deterministic Tubes
Zhengang Zhong, Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis
This paper presents a novel data-driven stochastic MPC design for discrete-time nonlinear systems with additive disturbances by leveraging the Koopman operator and a distributional…
Tube-based Distributionally Robust Model Predictive Control for Nonlinear Process Systems via Linearization
Zhengang Zhong, Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis
Model predictive control (MPC) is an effective approach to control multivariable dynamic systems with constraints. Most real dynamic models are however affected by plant-model mism…
Integrating process design and control using reinforcement learning
Steven Sachio, Max Mowbray, Maria Papathanasiou +2
To create efficient-high performing processes, one must find an optimal design with its corresponding controller that ensures optimal operation in the presence of uncertainty. When…
Safe model-based design of experiments using Gaussian processes
Panagiotis Petsagkourakis, Federico Galvanin
Construction of kinetic models has become an indispensable step in the development and scale up of processes in the industry. Model-based design of experiments (MBDoE) has been wid…
Chance Constrained Policy Optimization for Process Control and Optimization
Panagiotis Petsagkourakis, Ilya Orson Sandoval, Eric Bradford +3
Chemical process optimization and control are affected by 1) plant-model mismatch, 2) process disturbances, and 3) constraints for safe operation. Reinforcement learning by policy…
Input-Output Stability of Barrier-Based Model Predictive Control
Panagiotis Petsagkourakis, William P. Heath, Joaquin Carrasco +1
Conditions for input-output stability of barrier-based model predictive control of linear systems with linear and convex nonlinear (hard or soft) constraints are established throug…