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Neural Network-based Control Systems with Application to Flight Control, From Classical Neural Control to Reinforcement Learning — arXiv:2611.06429

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.06429
License (as archived): CC0-1.0
Archived: 2026-11-05T18:39:52.825Z
SHA-256 of the archived text: 161f153357fa6cb901a9912b732660d6e35881bfc57855b4b8ec5e4f007b9fdd
Stable URL of this page: https://metraipolis.com/documento/dc_b283f60b77cc

How to cite

Neural Network-based Control Systems with Application to Flight Control, From Classical Neural Control to Reinforcement Learning — arXiv:2611.06429 — arXiv (en), https://arxiv.org/abs/2611.06429 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_b283f60b77cc (Metraipolis archive dc_b283f60b77cc, sha256 161f153357fa6cb901a9912b732660d6e35881bfc57855b4b8ec5e4f007b9fdd)

BibTeX

@misc{dcb283f60b77cc,
  title = {Neural Network-based Control Systems with Application to Flight Control, From Classical Neural Control to Reinforcement Learning — arXiv:2611.06429},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.06429},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_b283f60b77cc; sha256 161f153357fa6cb901a9912b732660d6e35881bfc57855b4b8ec5e4f007b9fdd},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.06429. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

This book provides a comprehensive and systematic exploration of neural network-based intelligent control systems for aerospace applications, bridging foundational theory with practical implementation. It is structured into two primary parts: Part I covers model-based control design, detailing Feedback Error Learning (FEL), pseudocontrol strategies, and neural backstepping for strict-feedback systems, along with practical refinements such as learning rule modifications, composite learning, and discrete-time formulations, all grounded in rigorous Lyapunov stability analysis. Part II introduces model-free control, laying the groundwork for Reinforcement Learning and Approximate Dynamic Programming. Throughout, theoretical concepts are solidified through detailed case studies on quadrotor UAVs, fixed-wing aircraft (GTM), satellites, and hypersonic flight vehicles, complete with performance metrics, parameter sensitivity analyses, and discussions of actuator faults, input constraints, and wind disturbances, making the book an invaluable resource for students and practicing engineers seeking to design, simulate, and analyze intelligent flight control systems.

Tema degli esploratori: calcolo e infrastrutture (elenco arXiv eess.SY, nuovi lavori del giorno).
Categorie arXiv: Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2611.06429 — 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 2026-11-05T18:31:46.490Z dalla pagina https://arxiv.org/list/eess.SY/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).