8 citations · 13 across the 3 of their papers we have counts for
3 papers · 1 filter
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…
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…