汽车FlexRay车载网络控制系统工作的稳定性和可靠性研究

Research on the stability and reliability of FlexRay vehicle network control system

为提升汽车FlexRay车载网络的数据传输性能,保障其控制系统工作的稳定性与可靠性,提出一种新的汽车FlexRay车载网络控制系统工作的稳定性和可靠性研究方法。系统硬件部分由控制器与驱动器组成,系统软件利用模块化设计相应通信流程;以汽车FlexRay车载网络控制系统为被控对象,将LM算法与BP神经网络相结合,构建近似被控对象的神经网络预测模型,依据被控对象当前时刻工作状态预测下一时刻的工作状态,通过在线自适应调节工作量适应被控对象不同时刻的负载,提升其工作的稳定性与可靠性。实验结果显示该方法控制下,汽车FlexRay车载网络控制系统进入稳态工作所需的时间大致在0.05~0.06s左右,信息传输偏差在3μrad以内,说明该方法可提升FlexRay车载网络控制系统工作的稳定性和可靠性。

In order to improve the data transmission performance of the automotive FlexRay vehicle network and ensure the stability and reliability of its control system work,a new research method for the stability and reliability of the automotive FlexRay vehicle network control system is proposed.The hardware part of the system is composed of the controller and the driver,and the system software uses the modular design of the corresponding communication process.Taking the automotive FlexRay vehicle network control system as the controlled object,the LM algorithm and BP neural network are combined to build a neural network that approximates the controlled object.The prediction model predicts the working state of the next time according to the current state of the controlled object,and adapts the workload of the controlled object at different times through online adaptive adjustment of the workload to improve the stability and reliability of its work.The experimental results show that under the control of this method,the time required for the automotive FlexRay vehicle network control system to enter steady state operation is approximately 0.05~0.06s,and the information transmission deviation is within 3μrad,indicating that this method can improve the working of the FlexRay vehicle network control system.Stability and reliability.