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A Verification Method Of TTE Network About Communication Scheduling Based On DEVS Theory

Posted on:2021-10-23Degree:MasterType:Thesis
Country:ChinaCandidate:S F WangFull Text:PDF
GTID:2518306050471224Subject:Master of Engineering
Abstract/Summary:PDF Full Text Request
With the development of The Times,the Internet has become an indispensable part of People's Daily life.We use the traditional Ethernet to solve many needs in life,such as food,clothing,housing,transportation,etc..But the performance of the traditional Ethernet is not excellent in some industrial fields with high real-time requirements.The design of traditional Ethernet is based on the network structure of Event trigger,which cannot guarantee the reliability of data transmission once occurs.In the industrial field,especially in the aviation field,more real-time network is needed,such as the network based on Time trigger.TTE(Time Triggered Ethernet)networks conform to SAE AS602 international standard and have the advantages of high reliability,deterministic,and non-congestion.TTE network is not affected by event-triggered transmission and has been widely used in aviation.Simulation analysis of TTE network have to be carried out according to the actual modeling object.The commonly used simulation methods are: system simulation based on Petri net;Virtual simulation based on DEVS theory.Petri net system simulation can solve small-scale modeling simulation problems,but with the increase of network size and complexity,Petri net is easy to generate bottlenecks.The TTE network is hierarchical,concurrent and discrete.In view of the characteristics of TTE network,this paper adopts DEVS theory for simulation.DEVS is a discrete event system specification proposed by Zeigler in 1976.The established model has good adaptability and scalability,and can well describe the static and dynamic structure of the system,as well as the interaction process between the models,and can well describe the characteristics of TTE network.The simulation analysis of TTE network based on DEVS theory can simulate the transmission characteristics and network components of TTE network.Moreover,the schedulable feature of TTE network and the scheduling state of each sub-component can be well demonstrated.In addition,various network characteristics in TTE network,such as network load,network load fluctuation and network delay,can be analyzed.Features for TTE networks.In this paper,multi-objective optimization genetic algorithm is used to generate multiple scheduling policies that conform to TTE specification.Firstly,according to the constraint conditions of TTE network,the mathematical expression conforming to TTE network is constructed.Then,the corresponding objective functions of different network characteristics are constructed according to the network characteristics of TTE.Then,the fitness value of multi-objective optimization is calculated by using the objective function.Finally,the fitness value is used to generate TTE network scheduling policy by genetic algorithm.The newly generated scheduling table is compared with the original network scheduling.Dynamic and quantifiable,the scheduling of actual TTE network is analyzed and evaluated,and the optimal scheduling table optimization strategy is given.Complete the verification of TTE network communication scheduling.In the end,this paper summarizes some problems of TTE network simulation evaluation at the present stage,such as the lack of diversity of TTE network evaluation criteria,the incomplete Pareto solution set of weighted method in multi-objective optimization,and the lack of GPU and other techniques to improve computational power in the genetic algorithm.In future research,the above problems can be improved to improve work efficiency.Through the simulation analysis of TTE network,we can distinguish the excellent degree of network communication.It makes TTE network play a better role in aviation and other fields.It makes the real-time and reliability of TTE network reach a higher degree.
Keywords/Search Tags:TTE, DEVS, simulation, multi-objective optimization, genetic algorithm
PDF Full Text Request
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