Model‐based reinforcement learning for nonlinear optimal control with practical asymptotic stability guarantees-Reference-Cited by-同舟云学术

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Model‐based reinforcement learning for nonlinear optimal control with practical asymptotic stability guarantees

Published:2020-07-30 Issue:10 Volume:66 Page:
ISSN:0001-1541
Container-title:AIChE Journal
language:en
Short-container-title:AIChE J

Author:

Kim Yeonsoo¹^ORCID,Lee Jong Min¹^ORCID

Affiliation:

1. School of Chemical and Biological Engineering Institute of Chemical Processes, Seoul National University Seoul Republic of Korea

Funder

National Research Foundation of Korea

Publisher

Wiley

Subject

General Chemical Engineering,Environmental Engineering,Biotechnology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/aic.16544

Reference82 articles.

1. Reinforcement Learning for Optimal Feedback Control

2. Toward self‐driving processes: A deep reinforcement learning approach to control

3. Bringing new technologies and approaches to the operation and control of chemical process systems

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2. Supply responsive scheduling for ethylene cracking furnace systems based on deep reinforcement learning;AIChE Journal;2024-08-27

3. Stabilizing reinforcement learning control: A modular framework for optimizing over all stable behavior;Automatica;2024-06

4. AI-based optimal control of fed-batch biopharmaceutical process leveraging deep reinforcement learning;Chemical Engineering Science;2024-06

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