Open AccessPredictive control of FlexRay vehicle-mounted network based on neural network
Author(s) -
Yi Wang,
Min Chen,
Jianjun Ma,
Jigui Zhang,
Jiang Fu
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1907/1/012062
Subject(s) - flexray , artificial neural network , computer science , controller (irrigation) , reliability (semiconductor) , model predictive control , matlab , embedded system , engineering , control engineering , control (management) , artificial intelligence , automotive industry , agronomy , power (physics) , physics , quantum mechanics , biology , aerospace engineering , operating system
Aiming at the problem that the control performance and stability of the system can not guarantee the security and reliability of FlexRay network control system when the FlexRay vehicle network control system transmits data under heavy load. So FlexRay vehicle network prediction controller based on neural network is proposed. By predicting the current state of the vehicle, and the running status of the network at the next moment, it can adapt the dynamic workload of the vehicle network system in a way of adjusting the workload autonomously. The method uses a nonlinear neural network model to predict future model capacity. The controller calculates the control input, and by controlling the input, it optimize the performance of the network model in a certain period of time. According to the square result obtained by Matlab/Simulink, the neural network predictive control has good learning ability and self-adaptability, which can improve the performance of the FlexRay vehicle-mounted network control system.