A Power Electronic Converter Control Framework Based on Graph Neural Networks -- An Early Proof-of-Concept
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866908758853025792 |
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| author | Jakobeit, Darius Wallscheid, Oliver |
| author_facet | Jakobeit, Darius Wallscheid, Oliver |
| contents | Power electronic converter control is typically tuned per topology, limiting transfer across heterogeneous designs. This letter proposes a topology-agnostic meta-control framework that encodes converter netlists as typed bipartite graphs and uses a task-conditioned graph neural network backbone with distributed control heads. The policy is trained end-to-end via differentiable predictive control to amortize constrained optimal control over a distribution of converter parameters and reference-tracking tasks. In simulation on randomly sampled buck converters, the learned controller achieves near-optimal tracking performance relative to an online optimal-control baseline, motivating future extension to broader topologies, objectives, and real-time deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06686 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Power Electronic Converter Control Framework Based on Graph Neural Networks -- An Early Proof-of-Concept Jakobeit, Darius Wallscheid, Oliver Systems and Control Power electronic converter control is typically tuned per topology, limiting transfer across heterogeneous designs. This letter proposes a topology-agnostic meta-control framework that encodes converter netlists as typed bipartite graphs and uses a task-conditioned graph neural network backbone with distributed control heads. The policy is trained end-to-end via differentiable predictive control to amortize constrained optimal control over a distribution of converter parameters and reference-tracking tasks. In simulation on randomly sampled buck converters, the learned controller achieves near-optimal tracking performance relative to an online optimal-control baseline, motivating future extension to broader topologies, objectives, and real-time deployment. |
| title | A Power Electronic Converter Control Framework Based on Graph Neural Networks -- An Early Proof-of-Concept |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2601.06686 |