Machine Learning in Control Systems: An Overview of the State of the Art
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There has been recent progress in RL for control, which could be useful for fine-tuning geoengineering interventions such as deciding where and when to release aerosols.
“Additionally, there has been recent progress in RL for control [21,84,545], which could be useful for fine-tuning geoengineering interventions such as deciding where and when to release aerosols.”
From Tackling climate change with machine learning (2023) · cites this paper as [545]
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- Authors
- Moe, S., Rustad, A. M., & Hanssen, K. G.
- Affiliations
- SINTEF Digital (NO)
- Corresponding author
- Moe, Signe
- ORCID
- Hanssen, Kristian G.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 250-265
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2900527379
- Cited by
- 37
- References
- 43
- Citations by year
- 2025: 3 · 2024: 2 · 2023: 11 · 2022: 7 · 2021: 7 · 2020: 5 · 2019: 2
- FWCI
- 5.25
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Control of Nonlinear Systems
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Physical Sciences › Engineering › Control and Systems Engineering › Control Systems and Identification
- Keywords
- machine learning for control, neural network controller, feedback linearization, speed control, autonomous ships, model error, measurement noise, data-driven control
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