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Electrical Engineering and Systems Science > Systems and Control

arXiv:2211.10336 (eess)
[Submitted on 14 Nov 2022]

Title:Tire-road friction estimation and uncertainty assessment to improve electric aircraft braking system

Authors:Francesco Crocetti, G. Costante, M.L. Fravolini, P. Valigi
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Abstract:The accurate online estimation of the road-friction coefficient is an essential feature for any advanced brake control system. In this study, a data-driven scheme based on a MLP Neural Net is proposed to estimate the optimum friction coefficient as a function of windowed slip-friction measurements. A stochastic NN weights drop-out mechanism is used to online estimate the confidence interval of the estimated best friction coefficient thus providing a characterization of the epistemic uncertainty associated to the NN block. Open loop and closed loop simulations of the landing phase of an aircraft on an unknown surface are used to show the potentiality and efficacy of the proposed robust friction estimation approach.
Subjects: Systems and Control (eess.SY); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2211.10336 [eess.SY]
  (or arXiv:2211.10336v1 [eess.SY] for this version)
  https://doi.org/10.48550/arXiv.2211.10336
Related DOI: https://doi.org/10.1109/MED51440.2021.9480241

Submission history

From: Francesco Crocetti [view email]
[v1] Mon, 14 Nov 2022 10:36:14 UTC (1,110 KB)
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