A general infrastructure for data-driven control design and implementation in tokamaks

Joseph Abbate, Rory Conlin, Ricardo Shousha, Keith Erickson, Egemen Kolemen

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

A general infrastructure for tokamak controllers based on data-driven neural net models is presented. The paradigm allows for more flexible choices of both the underlying model and the desired controlled variables and targets. The system is implemented and tested on the DIII-D tokamak, enacting simultaneous pressure and temperature control via a finite-set model-predictive controller. Traditional control methods such as proportional-integral-derivative (PID) have proven effective for decoupled control tasks, but scale poorly when trying to achieve more complicated goals such as full state control. This is exactly where model-based controllers succeed.

Original languageEnglish (US)
Article number895890102
JournalJournal of Plasma Physics
Volume89
Issue number1
DOIs
StatePublished - Feb 17 2023

All Science Journal Classification (ASJC) codes

  • Condensed Matter Physics

Keywords

  • fusion plasma
  • plasma dynamics

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