State Estimation-Based Hybrid-Triggered Controller Design for Synchronization of Repeated Scalar Nonlinear Complex Dynamical Networks

Abstract

The goal of this work is to examine the issue of state estimation-based synchronization of nonlinear complex dynamical networks that are prone to external disturbances, repeated scalar nonlinearities and time-varying coupling delays. In a nutshell, hybrid-triggered communication transmission nonfragile control design with respect to the estimated states and extended dissipative theory is designed, which comprises of both the time and event-triggered mechanisms, and a hybrid generator is presented between the sensor and controller. More preciously, a stochastic variable satisfying the Bernoulli random binary distribution is utilized to represent the phenomenon of random transmission between the time and eventtriggered mechanism. Furthermore, delay-dependent adequate conditions for ensuring the synchronization of the addressed system are developed in the form of linear matrix inequalities. And, the requisite gain matrices and hybrid-triggered parameters are evaluated with the help of solving these procured conditions. Lastly, a numerical example with an application to memristor-based Chua's circuit model is demonstrated to ensure the effectiveness of established control technique.

Fixed‐time Synchronization of Stochastic Kuramoto‐Oscillator Networks With Switching Topology Under Dynamic Event‐triggered Scheme

“…Therefore, future work may focus on topology identification 48 and investigating the network coupled with a detail‐balanced graph 49 . In addition, the results in Reference 50 which employs a hybrid event‐triggered mechanism also inspires our next steps.…”

An Observer-Based Topology Identification and Synchronization in Finite Time for Fractional Singularly Perturbed Complex Networks via Dynamic Event-Triggered Control

“…For t ∈ [t i k , t i k+1 ), by computing Caputo fractional derivative of α-order of V 2 (t) along with the solution of system (7), one obtains…”

“…2). For DETM ( 6) with (7), set c = 1.004, other parameters are shown in Table 2. For system (7), take initial values η 1 (0) = 1.8, η 2 (0) = 2.7, η 3 (0) = 2.0 of the internal dynamic variable η(t).…”

“…Complex dynamical networks (CDNs), which contains a large amount of nodes with the communication topology, are ubiquitous in nature and human society, such as transportation networks, biological networks, power networks and so on [1][2][3][4]. Generally, the dynamics of CDNs are determined by the topology structure and dynamical properties of nodes, which indicates that the dynamical behavior of CDNs is difficult to understand when the communication topology is unknown [5][6][7]. Hence, it is significant to identify the topology of CDNs, see [8][9][10][11][12][13][14][15][16][17][18][19] and references therein.…”