Open AccessLM Algorithm Neural Network Predictive Control of FlexRay Bus System
Author(s) -
Zhichao Liu,
Yi Wang
Publication year - 2019
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 - 10.1088/1742-6596/1267/1/012094
Subject(s) - flexray , artificial neural network , computer science , robustness (evolution) , reliability (semiconductor) , embedded system , model predictive control , engineering , real time computing , automotive industry , control (management) , artificial intelligence , biochemistry , chemistry , power (physics) , physics , quantum mechanics , gene , aerospace engineering
In view of the uncertainty of data transmission due to the irregular data flow in the network and the limitation of network bandwidth resources, which makes the control performance and stability of FlexRay network decrease when data is transmitted at high speed and the reliability and safety of the control system can not be guaranteed, this paper proposes a method based on the Levenberg-Marquardt (LM) algorithm for neural network predictive control of the FlexRay bus. The method can predict the next moment operating state of the car network according to the current moment working state of the FlexRay car network to adaptively adjust task workload to adapt to changes in vehicle network system load and improve the reliability and stability of FlexRay network control system. The simulation results show that the neural network predictive control has good adaptability and robustness, which improves the control performance of the FlexRay automotive networked control system effectively.