FlexRay车载网络的神经网络模型参考控制

针对FlexRay车载网络控制系统的复杂性和非线性特点，有限的网络带宽资源会造成数据传输的不确定性和数据传输延时，使得FlexRay网络在高速传输数据时控制性能下降。利用神经网络具有的自学习、自适应和全局逼近的能力，本文以提高FlexRay车载网络控制性能为目的，提出以网络带宽利用率为参考模型的神经网络控制方法。首先对神经网络模型参考控制系统的结构进行分析，其次设计FlexRay车载网络的神经网络模型参考控制器，在负载的情况下，运用Matlab软件中的Simulink对控制器的性能进行仿真研究。仿真结果表明，该控制器能够有效地提高FlexRay车载网络控制性能，且对控制对象参数变化具有良好的适应性。  

Neural Network Model Reference Control of FlexRay Vehicle Network

Aiming at the complexity and nonlinear characteristics of FlexRay vehicle network control system, the limited network bandwidth resources result in the uncertainty of data transmission and the delay of data transmission, which makes the FlexRay network control performance degrade when the data is transmitted at high speed. Neural network has the ability of self-learning, adaptive and global approximation. In order to improve the performance of FlexRay vehicle network control, a neural network control method based on network bandwidth utilization is proposed. First, the structure of the neural network model reference control system is analyzed. Secondly, the neural network model reference controller of the FlexRay vehicle network is designed.In the case of load, the performance of the controller is simulated by using Simulink in Matlab software. The simulation results show that the controller can effectively improve the performance of FlexRay vehicle network control and have good adaptability to the change of the parameters of the control object.