Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/49148
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dc.creatorBarbosa, Bruno Henrique Groenner-
dc.creatorXu, Nan-
dc.creatorAskari, Hassan-
dc.creatorKhajepour, Amir-
dc.date.accessioned2022-02-02T18:51:59Z-
dc.date.available2022-02-02T18:51:59Z-
dc.date.issued2021-11-
dc.identifier.citationBARBOSA, B. H. G. et al. Lateral force prediction using gaussian process regression for intelligent tire systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems, [S.I.], 2021. DOI: 10.1109/TSMC.2021.3123310.pt_BR
dc.identifier.urihttps://ieeexplore.ieee.org/document/9609005pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/49148-
dc.description.abstractUnderstanding the dynamic behavior of tires and their interactions with roads plays an important role in designing integrated vehicle control strategies. Accordingly, having access to reliable information about tire-road interactions through tire-embedded sensors is desirable for developing enhanced vehicle control systems. Thus, the main objectives of this research are: 1) to analyze data from an experimental accelerometer-based intelligent tire acquired over a wide range of maneuvers, with different vertical loads, velocities, and high slip angles and 2) to develop a lateral force predictor based on a machine learning tool, more specifically, the Gaussian process regression (GPR) technique. It is determined that the proposed intelligent tire system can provide reliable information about the tire-road interactions even in the case of high slip angles. In addition, lateral force models based on GPR can predict forces very well, outperforming other machine learning models and providing levels of uncertainty that can be useful for designing vehicle control strategies.pt_BR
dc.languageenpt_BR
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)pt_BR
dc.rightsrestrictAccesspt_BR
dc.sourceIEEE Transactions on Systems, Man, and Cybernetics: Systemspt_BR
dc.subjectData analysispt_BR
dc.subjectGaussian processpt_BR
dc.subjectIntelligent tirept_BR
dc.subjectMachine learningpt_BR
dc.subjectTire lateral forcespt_BR
dc.subjectAnálise de dadospt_BR
dc.subjectProcesso gaussianopt_BR
dc.subjectPneus inteligentespt_BR
dc.subjectPneus - Força lateralpt_BR
dc.subjectAprendizado de máquinapt_BR
dc.titleLateral force prediction using gaussian process regression for intelligent tire systemspt_BR
dc.typeArtigopt_BR
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