{
  "annotation_id": "fb74c39f-2846-479a-a1c2-bb98747a14c2",
  "date_created": "2026-02-17T05:53:08.454000Z",
  "date_modified": "2026-02-17T05:53:08.454000Z",
  "file_hash": "86ebbf9568e78347a35f1724ca91504802c6697034a7bcbbfea0014865cb1d05",
  "private": false,
  "record": {
    "abstract": "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.",
    "arxiv_id": "2601.06686",
    "authors": [
      "Darius Jakobeit",
      "Oliver Wallscheid"
    ],
    "categories": [
      "eess.SY",
      "cs.SY"
    ],
    "license": "http://creativecommons.org/licenses/by-sa/4.0/",
    "title": "A Power Electronic Converter Control Framework Based on Graph Neural Networks -- An Early Proof-of-Concept",
    "url": "https://arxiv.org/abs/2601.06686",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "97f7b4d4-31a1-4592-ae89-f25f227e2aec",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}