FlexRay Vehicle Network Predictive Control Based on Neural Network

Li Huan, Li Chao, Yansong Wang
2018 MATEC Web of Conferences  
We propose a design method of FlexRay vehicle network forecasting control based on the neural network to solve the security and reliability of FlexRay network control system, where the control performance and stability of the system are reduced when transmiting data under heavy load, by sampling the working state of the vehicle network at the present time to predict the next-time network state, and adapting to the dynamic load in the vehicular network system by on-line adaptive workload
more » ... ve workload adjustment. The method used the nonlinear neural network model to predict the performance of the future model. The controller calculated the control input and optimized the performance of the next-time network model. The simulation results from the Matlab/Simulink showed that the neural network predictive control had good learning ability and adaptability. It could improve the performance of FlexRay vehicle network control system. ST o u J J J =+ (1) In equation (1), o J and u J are the number of losses of bits of the aforementioned two types, respectively. ST J is the number of loss of bits for the entire static segment.
doi:10.1051/matecconf/201823201042 fatcat:q2eyexpv5zey3m2q7y552aido4