Mixed-Integer MPC Strategies for Fueling and Density Control in Fusion Tokamaks Article Swipe
YOU?
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· 2023
· Open Access
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· DOI: https://doi.org/10.1109/lcsys.2023.3282891
Model predictive control (MPC) is promising for fueling and core density feedback control in nuclear fusion tokamaks, where the primary actuators, frozen hydrogen fuel pellets fired into the plasma, are discrete. Previous density feedback control approaches have only approximated pellet injection as a continuous input due to the complexity that it introduces. In this letter, we model plasma density and pellet injection as a hybrid system and propose two MPC strategies for density control: mixed-integer (MI) MPC using a conventional mixed-integer programming (MIP) solver and MPC utilizing our novel modification of the penalty term homotopy (PTH) algorithm. By relaxing the integer requirements, the PTH algorithm transforms the MIP problem into a series of continuous optimization problems, reducing computational complexity. Our novel modification to the PTH algorithm ensures that it can handle path constraints, making it viable for constrained hybrid MPC in general. Both strategies perform well with regards to reference tracking without violating path constraints and satisfy the computation time limit for real-time control of the pellet injection system. However, the computation time of the PTH-based MPC strategy consistently outpaces the conventional MI-MPC strategy.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/lcsys.2023.3282891
- OA Status
- green
- Cited By
- 9
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4379382638
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4379382638Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1109/lcsys.2023.3282891Digital Object Identifier
- Title
-
Mixed-Integer MPC Strategies for Fueling and Density Control in Fusion TokamaksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
C. Orrico, M. van Berkel, T.O.S.J. Bosman, W.P.M.H. Heemels, Dinesh KrishnamoorthyList of authors in order
- Landing page
-
https://doi.org/10.1109/lcsys.2023.3282891Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://research.tue.nl/nl/publications/ec87d576-c643-4c21-a41c-5d5c492094c9Direct OA link when available
- Concepts
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Model predictive control, Control theory (sociology), Integer programming, Computation, Solver, Mathematical optimization, Tokamak, Computer science, Algorithm, Mathematics, Control (management), Plasma, Physics, Artificial intelligence, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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9Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 3Per-year citation counts (last 5 years)
- References (count)
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28Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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