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Synchronization of Nonlinearly and Stochastically Coupled Markovian Switching Networks via Event-triggered Sampling

Abstract 

In this talk, we consider the exponential synchronization problem for a new array of nonlinearly and stochastically coupled networks via events-triggered sampling (ETS) by self adaptive learning. The networks include the following features: (1) a Bernoulli stochastic variable is introduced to describe the random structural coupling; (2) a stochastic variable with positive mean is used to model the coupling strength; (3) a continuous time homogeneous Markov chain is employed to characterize the dynamical switching of the coupling structure and pinned node sets. The proposed network model is capable to capture various stochastic effect of external environment during the network operations. In order to reduce networks’ workload, different ETS strategies for network self-adaptive learning are proposed under continuous and discrete monitoring respectively. Based on these ETS approaches, several suffificient conditions for synchronization are derived by employing stochastic Lyapunov Krasovskii functions, the properties of stochastic processes, and some linear matrix inequalities. 


报告人简介:董海玲,女,博士,硕士生导师,2008 年于中南大学获博士学位, 2012 于深圳大学晋升为副教授,2014 年入选深圳市高层次人才计划,2018 年入选深圳市孔雀人才计划,2017-2018 获国家留学基金委资助赴美国访学一年,并担任国际著名期刊 《IEEE TAC》《IEEE TNNL》《Nonlinear Analysis: Hybrid Systems 》《Nonlinear Dynamics》《Neural Networks》及国内期刊《应用概率统计》《控制理论与应用》匿名审稿人,主持和参加国家自然科学基金 2 项、广东省自然科学基金 2 项、深圳市基础研究基金 2 项。目前担任中国自动化学会控制理论专委会随机学组委员、广东省现场统计学会理事,发表学术论文 30 余篇,包括《IEEE TNNL 》长文1 篇,《Nonlinear Dynamics》长文 2 篇,《Neural Networks》长文 2 篇, ESI 高被引论文 1 篇。主要研究方向:随机过程与随机分析、随机系统的控制、复杂网络化系统的控制与状态估计等。