OptiML PSE
OptiML PSE
Research
Bayesian Optimization
Data-Driven Optimization
Supply Chain Optimization
Reinforcement Learning
Statistical Learning
Large Language Models
Hybrid Modelling
Process Control
Deep Learning in Chemical Engineering
News
People
Publications
Learning Resources
Contact
Uncertain dynamic systems
Tube-based distributionally robust model predictive control for nonlinear process systems via linearization
Model predictive control (MPC) is an effective approach to control multivariable dynamic systems with constraints. Most real dynamic …
Zhengang Zhong
,
Dr. Ehecatl Antonio del Rio Chanona
,
Panagiotis Petsagkourakis
Cite
DOI
URL
Tube-based distributionally robust model predictive control for nonlinear process systems via linearization
Model predictive control (MPC) is an effective approach to control multivariable dynamic systems with constraints. Most real dynamic …
Zhengang Zhong
,
Dr. Ehecatl Antonio del Rio Chanona
,
Panagiotis Petsagkourakis
Cite
DOI
URL
Constrained Reinforcement Learning for Dynamic Optimization under Uncertainty
Dynamic real-time optimization (DRTO) is a challenging task due to the fact that optimal operating conditions must be computed in real …
P. Petsagkourakis
,
Ilya Orson Sandoval
,
E. Bradford
,
D. Zhang
,
Dr. Ehecatl Antonio del Rio Chanona
Cite
DOI
URL
Cite
×