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Event-triggered Control and Online Learning for Networked Systems under Computational Delays — arXiv:2608.29576

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.29576
License (as archived): CC0-1.0
Archived: 2026-10-17T08:36:50.091Z
SHA-256 of the archived text: 70652331c4170917ca582d7a7a4717ef8eeca8679728ffbd45575609eace46d4
Stable URL of this page: https://metraipolis.com/documento/dc_bc6cf4eb57db

How to cite

Event-triggered Control and Online Learning for Networked Systems under Computational Delays — arXiv:2608.29576 — arXiv (en), https://arxiv.org/abs/2608.29576 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_bc6cf4eb57db (Metraipolis archive dc_bc6cf4eb57db, sha256 70652331c4170917ca582d7a7a4717ef8eeca8679728ffbd45575609eace46d4)

BibTeX

@misc{dcbc6cf4eb57db,
  title = {Event-triggered Control and Online Learning for Networked Systems under Computational Delays — arXiv:2608.29576},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.29576},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_bc6cf4eb57db; sha256 70652331c4170917ca582d7a7a4717ef8eeca8679728ffbd45575609eace46d4},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.29576. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Online learning-based control is a promising approach to control uncertain systems, where unknown components are identified during operation to improve control performance. However, resource-intensive online learning algorithms introduce non-negligible computational delays, especially when executed on systems with limited local computational resources. To mitigate this, an in-network online learning-based control structure is employed by deploying the learning-based controller on a remote computation node and connecting it via a communication channel. In this paper, control performance guarantee is first established by deriving tracking error bound for the in-network control architecture, while accounting for computational delays. The derived tracking error bound allows for diverse communication and computation strategies under a specific condition, including time-/event-triggered mechanisms. Additionally, the trade-off between communication and computation performances is shown for a given desired control performance. Furthermore, to enhance the efficiency in both communication and computation, an efficient control framework with an asynchronous event-triggered mechanism in both control and online learning is devised under the existence of computational delay. The proposed event-triggered strategy is proven to achieve the same control performance as time-triggered scenario while excluding Zeno behavior. Finally, we derive an explicit expression of the proposed event-trigger condition for exponentially stabilizable systems, and demonstrate its effectiveness through simulations.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-01 in cs.LG).
Categorie arXiv: Machine Learning (cs.LG) ; Robotics (cs.RO); Systems and Control (eess.SY).
Pagina del lavoro: https://arxiv.org/abs/2608.29576 — 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 2026-10-17T08:13:26.754Z dalla pagina https://arxiv.org/catchup/cs.LG/2026-09-01?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).