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AI design of tokamak operation for autonomous control of fusion plasma.

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AI control of tokamak fusion reactor

  • KSTAR is a tokamak (donut-shaped nuclear fusion reactor) located in South Korea.
  • This repository describes an AI that designs the tokamak operation trajectory to control the fusion plasma in KSTAR.
  • Here, we would like to control 3 physics parameters; βp, q95 and li.
  • I recommend you to see KSTAR Tokamak Simulator first. The manual control of it is replaced by AI here.

Installation

  • You can install by
$ git clone https://github.com/jaem-seo/AI_tokamak_control.git
$ cd AI_tokamak_control

1. Target arrival in 4 s interval

  • Open the GUI. It takes a bit (tens of secconds) depending on your environment.
$ python ai_control_v0.py

or

$ python ai_control_v1.py

  • Slide the toggles on the right to change the targets and click the "AI control" button (it takes tens of seconds).
  • Then, the AI will design the tokamak operation trajectory to achieve the given target in 4 s.

2. Real-time feedback target tracking

  • Open the GUI. It takes a bit (tens of secconds) depending on your environment.
$ python rt_control_v2.py

  • Slide the toggles on the right to change the target state.
  • Then, the AI will control the tokamak operation to track the targets in real-time.

Note

  • The AI was trained by reinforcement learning; TD3 and HER implementation from Stable Baselines.
  • The AI control can fail if the target state is physically unfeasible (ex. high-βp, low-q95 and high-li).
  • The tokamak simulation possesses most of the computation time, but the AI operation control is actually very fast (real-time capable in experiments).
  • Deployment on the KSTAR control system will require further development.

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