Use este identificador para citar ou linkar para este item: http://repositorio.ufla.br/jspui/handle/1/58214
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dc.creatorXu, Nan-
dc.creatorZhou, Jianfeng-
dc.creatorBarbosa, Bruno Henrique Groenner-
dc.creatorAskari, Hassan-
dc.creatorKhajepour, Amir-
dc.date.accessioned2023-07-27T20:48:08Z-
dc.date.available2023-07-27T20:48:08Z-
dc.date.issued2023-
dc.identifier.citationXU, N. et al. A soft sensor for estimating tire cornering properties for intelligent tires. IEEE Transactions on Systems, Man, and Cybernetics: Systems, [S.l.], 2023.pt_BR
dc.identifier.urihttps://ieeexplore.ieee.org/document/10149171pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/58214-
dc.description.abstractIntelligent tire systems are promising solutions for achieving precise vehicle state estimations, localization, and motion control in the context of autonomous driving. Tire cornering properties, namely, lateral force, aligning moment, and pneumatic trail, are crucial factors that should be accurately estimated for vehicle dynamics control purposes. In this work, a soft sensor for estimating tire cornering properties based on intelligent tire and machine learning is developed. The intelligent tire system is based on a triaxial accelerometer mounted on the inner liner of the tire tread, which provides acceleration measurements from the x, y, and z directions. Partial least squares and variable importance in the projection scores (PLS-VIP) are used in the feature extraction of the acceleration signals over the contact patch. A Gaussian process regression (GPR) model is trained to predict the cornering properties with confidence intervals under different input conditions. Based on the variances in the GPR predictions and minimum mean-square error criterion, a data fusion method for pneumatic trail estimation is proposed. It is demonstrated that the developed GPR models for cornering properties and the data fusion method for pneumatic trail estimation have satisfactory accuracy and reliability. The experimental results show that the soft sensor proposed in this work is a strong candidate for further applications in the development of vehicle state estimation and control algorithmspt_BR
dc.languageen_USpt_BR
dc.publisherIEEE Xplorept_BR
dc.rightsrestrictAccesspt_BR
dc.sourceIEEE Transactions on Systems, Man, and Cybernetics: Systemspt_BR
dc.subjectGaussian process regression (GPR)pt_BR
dc.subjectIntelligent tirept_BR
dc.subjectMachine learningpt_BR
dc.subjectSensing systemspt_BR
dc.subjectTire cornering propertiespt_BR
dc.titleA soft sensor for estimating tire cornering properties for intelligent tirespt_BR
dc.typeArtigopt_BR
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