Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/147406
Title: A sensorless state estimation for a safety-oriented cyber-physical system in urban driving : deep learning approach
Authors: Mohammad Al-Sharman
Murdoch, David
Cao, Dongpu
Lv, Chen
Zweiri, Yahya
Rayside, Derek
Melek, William
Keywords: Engineering::Mechanical engineering
Issue Date: 2021
Source: Mohammad 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.2020.1003474
Journal: IEEE/CAA Journal of Automatica Sinica
Abstract: In 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.
URI: https://hdl.handle.net/10356/147406
ISSN: 2329-9266
DOI: 10.1109/JAS.2020.1003474
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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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