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Learning to Tune with Biased Gradients: Iterative Feedback Tuning of Model-Free Control on Throttle Valves — arXiv:2611.06338

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.06338
License (as archived): CC0-1.0
Archived: 2026-11-05T18:17:54.101Z
SHA-256 of the archived text: 7a682da8c32186933d96b037665d1b8d3f7fba2beb8c17d2d6d9b43f61854562
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How to cite

Learning to Tune with Biased Gradients: Iterative Feedback Tuning of Model-Free Control on Throttle Valves — arXiv:2611.06338 — arXiv (en), https://arxiv.org/abs/2611.06338 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cdf508de2bc3 (Metraipolis archive dc_cdf508de2bc3, sha256 7a682da8c32186933d96b037665d1b8d3f7fba2beb8c17d2d6d9b43f61854562)

BibTeX

@misc{dccdf508de2bc3,
  title = {Learning to Tune with Biased Gradients: Iterative Feedback Tuning of Model-Free Control on Throttle Valves — arXiv:2611.06338},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.06338},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cdf508de2bc3; sha256 7a682da8c32186933d96b037665d1b8d3f7fba2beb8c17d2d6d9b43f61854562},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.06338. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Iterative feedback tuning (IFT) learns the parameters of a fixed-structure controller by stochastic gradient descent. At each iteration, two closed-loop experiments are run on the plant: the first one measures the tracking error, and the second one, driven by this error, provides the sensitivities from which the gradient of the cost is estimated. This estimate is unbiased for linear plants only. We study the biased case on automotive electronic throttle valves, whose friction and stiction vary between valves and runs, controlled by an intelligent proportional (model-free) controller, which is an incremental PI controller. We show that the mismatch of the controller state at the start of the two experiments, which stiction makes unavoidable, adds an error inversely proportional to the amplitude of the second experiment. On digital twins of ten valves, the IFT gradient is a descent direction in only 69\% of the low-cost region, friction being the cause, and all update rules plateau and then drift. On two hardware valves, the best update rule depends on the noise structure of the gradient. On the first valve, where one gradient component is buried in noise, Adam outperforms Gauss--Newton, its normalisation and its momentum each improving one parameter. On the second valve, where both components are consistent, the same normalisation amplifies a direction that Gauss--Newton rightly discounts, and Adam drifts to a controller worse than the initial one. The twins reproduce the mechanism but not this ranking.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2611.06338 — 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 2026-11-05T18:12:49.055Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).