142 citations · 196 across the 6 of their papers we have counts for
8 papers
Hybrid Gaussian Process Modeling Applied to Economic Stochastic Model Predictive Control of Batch Processes
E. Bradford, L. Imsland, M. Reble +1
Nonlinear model predictive control (NMPC) is an efficient approach for the control of nonlinear multivariable dynamic systems with constraints, which however requires an accurate p…
Combining Gaussian processes and polynomial chaos expansions for stochastic nonlinear model predictive control
E. Bradford, L. Imsland
Model predictive control is an advanced control approach for multivariable systems with constraints, which is reliant on an accurate dynamic model. Most real dynamic models are how…
Real-Time Optimization Meets Bayesian Optimization and Derivative-Free Optimization: A Tale of Modifier Adaptation
Ehecatl Antonio del Rio-Chanona, Panagiotis Petsagkourakis, Eric Bradford +2
This paper investigates a new class of modifier-adaptation schemes to overcome plant-model mismatch in real-time optimization of uncertain processes. The main contribution lies in…
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…
Constrained Reinforcement Learning for Dynamic Optimization under Uncertainty
Panagiotis Petsagkourakis, Ilya Orson Sandoval, Eric Bradford +2
Dynamic real-time optimization (DRTO) is a challenging task due to the fact that optimal operating conditions must be computed in real time. The main bottleneck in the industrial a…
Stochastic data-driven model predictive control using Gaussian processes
E. Bradford, L. Imsland, D. Zhang +1
Nonlinear model predictive control (NMPC) is one of the few control methods that can handle multivariable nonlinear controlsystems with constraints. Gaussian processes (GPs) presen…