摘要
分析了TTCAN网络平台对于周期性消息及非周期性消息的调度策略,分别对其进行改进,并通过遗传算法对系统矩阵周期调度表进行优化,之后利用Simulink中的Stateflow工具针对一般工业控制系统建立了TTCAN网络调度仿真平台,并对仿真平台进行优化,优化后的仿真平台较好地提高了网络系统带宽利用率,增大了总线通信量,降低了各传感器节点周期性消息的响应时间和非周期性消息的延时时间,从而改善了网络平台的通信实时性能.
This paper analyzes the scheduling strategy in the TTCAN network platform for periodic message and nonperiodic message and improves the scheduling strategy respectively, at the same time optimizes the system matrix through genetic algorithm, and then uses the tool Stateflow in the Simulink to establish a TTCAN network scheduling simulation platform for general industrial control system, and optimizes the platform, the optimized simulation platform better improved the network＇s bandwidth utilization, and increased the bus communications traffic, reduced the response time of the sensor nodes＇ periodic message and the delay time of the sensor nodes＇ nonperiodic message. Consequently, this optimization improved the network platform＇s real-time eolnmunication performance.
出处
《微电子学与计算机》
CSCD
北大核心
2010年第3期67-70,共4页
Microelectronics & Computer
关键词
调度策略
遗传算法
优化
仿真
传感器节点
scheduling algorithm
GA
optimization
simulation
sensor nodes
