Deep hybrid model‐based predictive control with guarantees on domain of applicability-Reference-Cited by-同舟云学术

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Deep hybrid model‐based predictive control with guarantees on domain of applicability

Published:2023-01-19 Issue:5 Volume:69 Page:
ISSN:0001-1541
Container-title:AIChE Journal
language:en
Short-container-title:AIChE Journal

Author:

Bangi Mohammed Saad Faizan¹,Kwon Joseph Sang‐Il¹^ORCID

Affiliation:

1. Artie McFerrin Department of Chemical Engineering Texas A&M University College station Texas USA

Publisher

Wiley

Subject

General Chemical Engineering,Environmental Engineering,Biotechnology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/aic.18012

Reference47 articles.

1. Review on data-driven modeling and monitoring for plant-wide industrial processes

2. Data-driven modeling and learning in science and engineering

3. Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches

4. Process systems engineering – The generation next?

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

Cited by 28 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Empowering Hybrid Models with Attention-Based Time-series Transformers: A Case Study in Batch Crystallization;2024 American Control Conference (ACC);2024-07-10

2. Lyapunov-Based Model Predictive Control Using Operable Adaptive Sparse Identification of Systems (OASIS);2024 American Control Conference (ACC);2024-07-10

3. A Hybrid Modeling Framework for Catalytic Systems: Sensitivity Analysis and Estimation of Activation Energies;2024 American Control Conference (ACC);2024-07-10

4. Integrating Deep Neural Networks for Hybrid Modeling of Complex Chemical Processes: Estimation of Spatiotemporally Varying Parameters in Moving Boundary Problems;2024 American Control Conference (ACC);2024-07-10

5. A data-driven framework integrating Lyapunov-based MPC and OASIS-based observer for control beyond training domains;Journal of Process Control;2024-06

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