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Flatness-based Neural Network Control of DC-DC Converters — arXiv:2609.39849

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.39849
License (as archived): CC0-1.0
Archived: 2026-10-07T16:05:20.323Z
SHA-256 of the archived text: a26cd37af412f080f836cdbe3ee361e94046c9fa56fb6c2888c18aafb7f9d38c
Stable URL of this page: https://metraipolis.com/documento/dc_cad46622f736

How to cite

Flatness-based Neural Network Control of DC-DC Converters — arXiv:2609.39849 — arXiv (en), https://arxiv.org/abs/2609.39849 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cad46622f736 (Metraipolis archive dc_cad46622f736, sha256 a26cd37af412f080f836cdbe3ee361e94046c9fa56fb6c2888c18aafb7f9d38c)

BibTeX

@misc{dccad46622f736,
  title = {Flatness-based Neural Network Control of DC-DC Converters — arXiv:2609.39849},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.39849},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cad46622f736; sha256 a26cd37af412f080f836cdbe3ee361e94046c9fa56fb6c2888c18aafb7f9d38c},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.39849. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

In this work, we present a simple method to engineer a small neural network to control power converters. Learning-based methods have emerged as a promising approach for power converter control over wide operating point variations. However, many of these approaches do not exploit the analytical structure of converter dynamics and, therefore, search over unnecessarily broad policy classes. We address this gap with a systematic framework that combines model-based trajectory generation with policy learning. First, we use differential flatness of averaged converter models to generate feasible state and constrained duty cycle trajectories where duty acts as a control effort signal. Then for policy learning, we use these model-generated trajectories to construct labeled state-to-duty data and train a multilayer perceptron to approximate the control law with a static map. We apply this framework across buck, boost, and buck-boost converters through topology-dependent flat output selection and a common learning pipeline. Simulations show close agreement between learned and model-generated duty trajectories and stable regulation under large synchronous input and load disturbances.

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-10-01 in eess.SY).
Categorie arXiv: Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2609.39849 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-07T15:38:32.034Z dalla pagina https://arxiv.org/catchup/eess.SY/2026-10-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).