Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147406
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dc.contributor.authorMohammad Al-Sharmanen_US
dc.contributor.authorMurdoch, Daviden_US
dc.contributor.authorCao, Dongpuen_US
dc.contributor.authorLv, Chenen_US
dc.contributor.authorZweiri, Yahyaen_US
dc.contributor.authorRayside, Dereken_US
dc.contributor.authorMelek, Williamen_US
dc.date.accessioned2021-03-31T05:15:25Z-
dc.date.available2021-03-31T05:15:25Z-
dc.date.issued2021-
dc.identifier.citationMohammad Al-Sharman, Murdoch, D., Cao, D., Lv, C., Zweiri, Y., Rayside, D. & Melek, W. (2021). A sensorless state estimation for a safety-oriented cyber-physical system in urban driving : deep learning approach. IEEE/CAA Journal of Automatica Sinica, 8(1), 169-178. https://dx.doi.org/10.1109/JAS.2020.1003474en_US
dc.identifier.issn2329-9266en_US
dc.identifier.urihttps://hdl.handle.net/10356/147406-
dc.description.abstractIn today's modern electric vehicles, enhancing the safety-critical cyber-physical system CPS 's performance is necessary for the safe maneuverability of the vehicle. As a typical CPS, the braking system is crucial for the vehicle design and safe control. However, precise state estimation of the brake pressure is desired to perform safe driving with a high degree of autonomy. In this paper, a sensorless state estimation technique of the vehicle's brake pressure is developed using a deep-learning approach. A deep neural network DNN is structured and trained using deep-learning training techniques, such as, dropout and rectified units. These techniques are utilized to obtain more accurate model for brake pressure state estimation applications. The proposed model is trained using real experimental training data which were collected via conducting real vehicle testing. The vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving cycles. Based on these experimental data, the DNN is trained and the performance of the proposed state estimation approach is validated accordingly. The results demonstrate high-accuracy brake pressure state estimation with RMSE of 0.048 MPa.en_US
dc.language.isoenen_US
dc.relation.ispartofIEEE/CAA Journal of Automatica Sinicaen_US
dc.rights© 2021 Chinese Association of Automation. All rights reserved. This paper was published in Journal of Automatica Sinica and is made available with permission of Chinese Association of Automation.en_US
dc.subjectEngineering::Mechanical engineeringen_US
dc.titleA sensorless state estimation for a safety-oriented cyber-physical system in urban driving : deep learning approachen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.identifier.doi10.1109/JAS.2020.1003474-
dc.description.versionPublished versionen_US
dc.identifier.scopus2-s2.0-85097131161-
dc.identifier.issue1en_US
dc.identifier.volume8en_US
dc.identifier.spage169en_US
dc.identifier.epage178en_US
dc.subject.keywordsBrake Pressure State Estimationen_US
dc.subject.keywordsCyber-physical System (CPS)en_US
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