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A Sensorless State Estimation for A Safety-Oriented Cyber-Physical System in Urban Driving:Deep Learning approach

IEEE/CAA Journal of Automatica Sinica 2021年第1期8卷 169-178页

作者： Mohammad Al-Sharman David Murdoch Dongpu Cao Chen Lv Yahya Zweiri Derek Rayside William Melek the Department of Electrical and Computer Engineering University of WaterlooWaterloo N2L 3G1Canada the Department of Mechanical and Mechatronics Engineering University of WaterlooWaterloo N2L 3G1Canada the Cognitive Autonomous Driving Lab University of WaterlooWaterloo N2L 3G1Canada the School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 999002Singapore the Faculty of Science Engineering and ComputingKingston University LondonLondon SW153DWUK the Khalifa University Center for Autonomous Robotic Systems Department of Aerospace EngineeringKhalifa UniversityAbu Dhabi 127788UAE

In today's modern electric vehicles,enhancing the safety-critical cyber-physical system(CPS)'s performance is necessary for the safe maneuverability of the *** a typical CPS,the braking system is crucial for the vehicle design and safe ***,precise state estimation of the brake pressure is desired to perform safe driving with a high degree of *** 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 *** techniques are utilized to obtain more accurate model for brake pressure state estimation *** proposed model is trained using real experimental training data which were collected via conducting real vehicle *** vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving *** on these experimental data,the DNN is trained and the performance of the proposed state estimation approach is validated *** results demonstrate high-accuracy brake pressure state estimation with RMSE of 0.048 MPa.

关键词： Brake pressure state estimation cyber-physical system(CPS) deep learning dropout regularization approach

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