Synchronization Issue's Research Of The Complex Networks Base On Event-triggered Sample Strategy In Random Environment

The synchronization phenomenon of the complex networks is a typical collective behavior.In recent decades,the problem of the synchronization for the complex networks has been investigated widely.However,most of the literatures about synchronization are built upon the continuous communication model.Namely,the information between the nodes in the network is updated and transmitted immediately,which not only leads to unnecessary bandwidth and energy consumption but also increases the communication load of the network and decreases the network anti-jamming ability.So,considering the discrete sampling strategy has more practical significance.This thesis aims to investigate the synchronization issue for the complex networks with Markov switching coupling in the random environment via event-triggered sampling strategy.The main contents are organized as follows:Firstly,the mean square exponential synchronization problem for nonlinearly and stochastically coupled complex networks is investigated.The randomness of the network model is mainly embodied in two aspects: 1)The random occurring coupling and the random coupling strength are described by a sequence of Bernoulli random variables and a sequence of normal random variables respectively;2)The coupling structure and the controlled node sets are modulated by a Markov chain.Under continuous and discrete monitoring,we design two event-triggered rules based on two different measurement error upper bound separately.According to Lyapunov stability theory,the stochastic process theory and Gronwall-Bellman inequality,the sufficient conditions for the mean square exponential synchronization are derived.Finally,a numerical example contained 100 nodes is given to verify the effectiveness of the theory and the superiority of four eventtriggered rules.Secondly,we discuss the synchronization problem for the delayed complex networks with Markov switching coupling.In the network model,the transition rates are partially unknown,and the synchronization trajectory is dynamically adjusted,meanwhile a new randomly occurring event-triggered control strategy is proposed to control the nodes with the probability p.By constructing a novel stochastic Lyapunov-Krasovskii function,some exponential synchronization criteria are obtained in terms of the stochastic process theory,Lyapunov stability theory and the famous Halanay inequality.Furthermore,the positive lower bound of the event-triggered interval is given,which can exclude the Zeno behaviors.A numerical simulation shows that the theory is effective.Finally,based on the decentralized and centralized events-triggered sampling strategy separately,the issue of synchronization for the Markov switching coupled complex networks with the stochastic noise and partially unknown transition rates is researched.The stochastic noise is existed in the signal transmission process among the nodes and described by a Brownian movement.According to the measurement error and the synchronization error,a centralized event-triggered rule and a decentralized event-triggered rule are provided separately.By using stability theory and It?-Doeblin formula,some exponential synchronization criteria are obtained.A simulation example is provided to demonstrate the effectiveness of the theory.