Sim-to-real transfer reinforcement learning for control of thermal effects of an atmospheric pressure plasma jet
Problem
Feedback control of thermal effects of atmospheric pressure plasma jets on complex substrates, which is difficult due to complex plasma-substrate interactions.
Why it matters
Plasma treatment of substrates is highly sensitive to substrate properties, so precise thermal control determines treatment quality.
What the AI did
A deep reinforcement learning agent was trained via sim-to-real transfer on an experimentally-validated physics-based model to perform temperature setpoint tracking on substrates with different characteristics.
Results
not stated in source
Impact
Demonstrates RL as a promising approach for learning-based control of plasma jets, per abstract.
Record
| Venue | Plasma Sources Science and Technology |
|---|---|
| Published | 2019 |
| Work type | article |
| Authors | Matthew David Witman, Dogan Gidon, David B. Graves, Berend Smit, Ali Mesbah |
| Citations | 46 (OpenAlex, at import time) |
| References | 27 |
| Model or method | Deep reinforcement learning (specific algorithm not stated in source) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1088/1361-6595/ab3c15 |
| Open access | green |
| Keywords | reinforcement learning, sim-to-real transfer, plasma jet, thermal control, atmospheric pressure plasma |
Importance rating
real but narrower advance
Precise thermal control of plasma jets matters for treatment quality on complex substrates; a real but narrow engineering control advance, no scale stated.
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