FlexRay车载网络的神经网络模型参考控制
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摘要
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。
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.
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.
引文
[1] 戴晓晨,张凤登,张宇惠. 基于FlexRay总线线控制动系统的设计研究[J]. 电子测量技术, 2017,40(12):6-10.
[2] 韩强,雷艾,牛德青. FlexRay静态段优化算法[J]. 兵工自动化, 2011,30(3):42-44.
[3] 赵睿,秦贵和,范铁虎. FlexRay通信协议的总线周期优化[J]. 计算机应用研究, 2010,27(10):3847-3850.
[4] 卞永明,蒋佳,徐新明. FlexRay总线在工程车辆中的应用研究[J]. 中国工程机械学报, 2010,8(10):27-30.
[5] YANG J H, CHEN G W. Application research based on FlexRay bus for all-electronic computer interlocking system of railway signal[J]. Advanced Materials Research, 2013,671:3179-3184.
[6] 张秀玲. 神经网络非线性系统模型参考自适应控制器统一设计法[J]. 控制与决策, 2002,17(2):151-154.
[7] TRAN T, HA Q P. Networked control systems with accumulative quadratic constraint[J]. Electronics Letters, 2011,47(2):108-110.
[8] SUN Y, QIN S. Stability of networked control systems with packet dropout: An average dwell time approach[J]. IET Control Theory and Applications, 2011,5(1):47-53.
[9] 孔祥琦. FlexRay静态段与动态段的消息调度优化研究[D]. 延吉:延边大学, 2018.
[10] 白卫伟. 车载FlexRay总线通信机制优化研究[D]. 北京:北京交通大学, 2018.
[11] LIU G Y, XU B G. A real-time scheduling scheme of quantized control systems under bandwidth constraints[C]// Proceedings of the 8th IEEE World Congress on Intelligent Control and Automation. 2010:2334-2338.
[12] 景忠玉. FlexRay汽车总线浅析(下)[J]. 汽车维修与保养, 2018(6):84-85.
[13] 景忠玉. FlexRay汽车总线浅析(上)[J]. 汽车维修与保养, 2018(4):69-71.
[14] 叶鹏. 浅析FlexRay总线在乘用车中的应用[J]. 山东工业技术, 2017(23):105.
[15] 王刚,丁天宝,荣健,等. FlexRay网络中带宽利用率的理论研究[J]. 计算机应用, 2011,31(10):2634-2637.
[16] 朱晔. 基于消息调度的FlexRay车载网络可靠性研究[D]. 长沙:湖南大学, 2013.
[17] 张建军,于萍,张本宏. 基于带宽利用率的FlexRay静态段研究[J]. 计算机应用研究, 2012,29(12):4574-4576.
[18] 贾超. 基于神经网络的多模型自适应控制方法研究[D]. 北京:北京科技大学, 2017.
[19] 郑晋平. 基于RBF神经网络辨识的单神经元PID模型参考自适应控制[J]. 电子技术与软件工程, 2015(19):18-19.
[20] 张敏,徐启华. 基于改进BP的神经网络模型参考自适应控制[J]. 软件, 2015,36(7):118-123.
[21] 张威,施伟锋,许丽霞. 船舶发电机组转速神经网络模型参考自适应控制研究[J]. 高技术通讯, 2014,24(10):1089-1095.
[22] 郭庆鼎,王军. 基于在线辨识补偿的永磁直线同步电机模型参考自适应神经网络速度控制[J]. 电气传动, 2000(4):16-19.
[23] 刘建军. 基于径向基神经网络的机电系统精确模型辨识方法研究[D]. 哈尔滨:哈尔滨工业大学, 2017.
[2] 韩强,雷艾,牛德青. FlexRay静态段优化算法[J]. 兵工自动化, 2011,30(3):42-44.
[3] 赵睿,秦贵和,范铁虎. FlexRay通信协议的总线周期优化[J]. 计算机应用研究, 2010,27(10):3847-3850.
[4] 卞永明,蒋佳,徐新明. FlexRay总线在工程车辆中的应用研究[J]. 中国工程机械学报, 2010,8(10):27-30.
[5] YANG J H, CHEN G W. Application research based on FlexRay bus for all-electronic computer interlocking system of railway signal[J]. Advanced Materials Research, 2013,671:3179-3184.
[6] 张秀玲. 神经网络非线性系统模型参考自适应控制器统一设计法[J]. 控制与决策, 2002,17(2):151-154.
[7] TRAN T, HA Q P. Networked control systems with accumulative quadratic constraint[J]. Electronics Letters, 2011,47(2):108-110.
[8] SUN Y, QIN S. Stability of networked control systems with packet dropout: An average dwell time approach[J]. IET Control Theory and Applications, 2011,5(1):47-53.
[9] 孔祥琦. FlexRay静态段与动态段的消息调度优化研究[D]. 延吉:延边大学, 2018.
[10] 白卫伟. 车载FlexRay总线通信机制优化研究[D]. 北京:北京交通大学, 2018.
[11] LIU G Y, XU B G. A real-time scheduling scheme of quantized control systems under bandwidth constraints[C]// Proceedings of the 8th IEEE World Congress on Intelligent Control and Automation. 2010:2334-2338.
[12] 景忠玉. FlexRay汽车总线浅析(下)[J]. 汽车维修与保养, 2018(6):84-85.
[13] 景忠玉. FlexRay汽车总线浅析(上)[J]. 汽车维修与保养, 2018(4):69-71.
[14] 叶鹏. 浅析FlexRay总线在乘用车中的应用[J]. 山东工业技术, 2017(23):105.
[15] 王刚,丁天宝,荣健,等. FlexRay网络中带宽利用率的理论研究[J]. 计算机应用, 2011,31(10):2634-2637.
[16] 朱晔. 基于消息调度的FlexRay车载网络可靠性研究[D]. 长沙:湖南大学, 2013.
[17] 张建军,于萍,张本宏. 基于带宽利用率的FlexRay静态段研究[J]. 计算机应用研究, 2012,29(12):4574-4576.
[18] 贾超. 基于神经网络的多模型自适应控制方法研究[D]. 北京:北京科技大学, 2017.
[19] 郑晋平. 基于RBF神经网络辨识的单神经元PID模型参考自适应控制[J]. 电子技术与软件工程, 2015(19):18-19.
[20] 张敏,徐启华. 基于改进BP的神经网络模型参考自适应控制[J]. 软件, 2015,36(7):118-123.
[21] 张威,施伟锋,许丽霞. 船舶发电机组转速神经网络模型参考自适应控制研究[J]. 高技术通讯, 2014,24(10):1089-1095.
[22] 郭庆鼎,王军. 基于在线辨识补偿的永磁直线同步电机模型参考自适应神经网络速度控制[J]. 电气传动, 2000(4):16-19.
[23] 刘建军. 基于径向基神经网络的机电系统精确模型辨识方法研究[D]. 哈尔滨:哈尔滨工业大学, 2017.