Reinforcement learning based optimal control of batch processes using Monte-Carlo deep deterministic policy gradient with phase segmentation

A model-based deep reinforcement learning method applied to finite-horizon optimal control of nonlinear control-affine system

(2020) Jong Woo Kim et al.   JOURNAL OF PROCESS CONTROL

Towards Self‐Driving Processes: A Deep Reinforcement Learning Approach to Control

(2019) Steven Spielberg et al.   AICHE JOURNAL

Reinforcement learning for batch bioprocess optimization

(2019) P. Petsagkourakis et al.   COMPUTERS & CHEMICAL ENGINEERING

Dual robust nonlinear model predictive control: A multi-stage approach

(2018) S. Thangavel et al.   JOURNAL OF PROCESS CONTROL

Nonlinear model predictive control of fed-batch fermentations using dynamic flux balance models

(2016) Liang Chang et al.   JOURNAL OF PROCESS CONTROL

Mastering the game of Go with deep neural networks and tree search

(2016) David Silver et al.   NATURE

Method of moments: A versatile tool for deterministic modeling of polymerization kinetics

(2015) Erlita Mastan et al.   EUROPEAN POLYMER JOURNAL

Human-level control through deep reinforcement learning

(2015) Volodymyr Mnih et al.   NATURE

A tutorial review of economic model predictive control methods

(2014) Matthew Ellis et al.   JOURNAL OF PROCESS CONTROL

Handling uncertainty in economic nonlinear model predictive control: A comparative case study

(2014) Sergio Lucia et al.   JOURNAL OF PROCESS CONTROL

Reactor modeling and recipe optimization of polyether polyol processes: Polypropylene glycol

(2013) Yisu Nie et al.   AICHE JOURNAL

Multi-stage nonlinear model predictive control applied to a semi-batch polymerization reactor under uncertainty

(2013) Sergio Lucia et al.   JOURNAL OF PROCESS CONTROL

Tube-based robust nonlinear model predictive control

(2011) D. Q. Mayne et al.   INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL