The Research of Vehicle Network Control System Model

Author(s):  
Kai Li ◽  
Juan Wan ◽  
Jie Bai ◽  
Jianxian Chen ◽  
Gan Chen ◽  
...  
2018 ◽  
Vol 232 ◽  
pp. 01042 ◽  
Author(s):  
Li Huan ◽  
Li Chao

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 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.


Author(s):  
A. K. Kanaev ◽  
◽  
A. N. Gorbach ◽  
E. V. Oparin, ◽  
◽  
...  

2014 ◽  
Vol 548-549 ◽  
pp. 819-823
Author(s):  
Xi Juan Wang ◽  
Tao Zhou ◽  
Jing Xiao Feng ◽  
Yu Peng Pei

In the AC control system, vector control theory is very popular as it makes the AC motor achieve the performance as perfect as DC motor [1]. In the paper, the vector control theory is briefly introduced, and then a vector control system model is builded in the matlab/simulink, and the SVPWM technique is adopted. The results show that the improved vector control sytem of PMSM has a excellent performance.


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