Finite‐time synchronization and identification of the Markovian switching delayed network with multiple weights

Abstract

This paper focuses on finite‐time synchronization and parameters identification in the Markovian switching complex delayed network with multiple weights. Considering the control cost required for network synchronization, finite‐time control technique and pinning control strategy are adopted. Based on these methods, when finite‐time synchronization of the network is achieved, the unknown parameters of the system can also be identified in finite time. In addition, in order to solve the problem of the network performance change caused by network topology switching, an Optimal Nodes Selection Control Strategy (ONSCS) is proposed. On the one hand, finite‐time synchronization and identification of the network can be realized faster, on the other hand, it also further reduces the energy consumption and control cost of the network. Finally, two sets of comparative numerical simulations are given to prove the superiority and applicability of the proposed ONSCS.

Low Energy Cost Synchronization Strategy for Markovian Switching Complex Systems/Networks: Multiple Perspectives Comparative Analysis

“…Based on the optimal node control strategy proposed in the previous research of the authors [22], combined with the Markovian switching complex network characteristics, sorting according to the node importance measurement indicators at different time intervals, some important nodes (nodes with greater impact on system synchronization) are selected as controlled nodes, and we then establish network models at different time intervals and design a controller with a simpler structure to achieve synchronization with low control cost and low energy consumption.…”

Graph‐theoretic approach to finite‐time synchronization and topology identification of delayed multi‐group models with stochastic perturbation and multiple dispersal

“…Finite-time control technology not only considers the performance of systems from the perspective of the optimal time but also saves working time and reduces resource costs. Based on the above reasons, finite-time stability [20][21][22], fixed-time stability [23][24][25], and finite-time synchronization and identification [26][27][28][29] make more sense in practice. Especially considering the case of stochastic perturbation in the systems, Chen et al defined stochastic settling time function and proposed stochastically finite-time attractiveness [30].…”