Event-triggered control for coupled reaction–diffusion complex network systems with finite-time synchronization

Luo, Yiping; Yao, Yuejie; Cheng, Zifeng; Xiao, Xing; Liu, Hanyu

doi:10.1016/j.physa.2020.125219

Cited by 26 publications

(2 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It should be noted, however, that many proven conclusions pertaining to event-triggering techniques are in the framework of continuous-time and discrete-time systems [34]. Using the event-triggered approach, beneficial results on the investigation of SNNs for several stability issues, such as finite-time synchronization [30,32,35,36], have been established. Meanwhile, due to the implementation, limitations of the system equipment, and impact of environments, some fluctuations certainly happen in the control process of SNNs.…”

Section: Introductionmentioning

confidence: 99%

Event‐triggered synchronization for stochastic delayed neural networks: Passivity and passification case

Vadivel

Hammachukiattikul

Zhu

et al. 2022

Asian Journal of Control

View full text Add to dashboard Cite

This article investigates the event-triggered synchronization problem of stochastic neural networks under passivity and passification cases. For saving communication resources, an event-triggered approach is engaged in the design of synchronization for the delayed stochastic neural networks. To decrease network trouble, an event-triggered scheme is suggested between the sampler and communication network. A nonfragile event-triggered controller is intended to guarantee the finite-time stability of the subsequent closed-loop system. By applying the Lyapunov-Krasovkii functional (LKF) and the novel integral inequalities, a stability criteria for an interval-time varying delay error system ensure the designed controller can fulfill the necessities of passivity and passification performance. The desired control gain and event-triggered parameters are then found based on the linear matrix inequalities (LMIs). Finally, illustrative examples are given to show the benefits and validity of the desired control law.

show abstract

Section: Introductionmentioning

confidence: 99%

Event‐triggered synchronization for stochastic delayed neural networks: Passivity and passification case

Vadivel

Hammachukiattikul

Zhu

et al. 2022

Asian Journal of Control

View full text Add to dashboard Cite

show abstract

“…Furthermore, we suppose t N = nk, h x = h y = h, n = 0, 1, ...NT , while t would be the time step. The (l, m) index of estimations is utilized to represent the finite difference approach, whereas the index that utilizes the inherent enumeration of the base stations is employed in the corresponding linear frameworks [41]. The approximation or numerical solution sought will be obtained only at the grid points and the time levels, which is denoted by z n l,m = z(x l,m , t n ) such that l, m indicates the locations of the grid points, and n presents the time levels.…”

Section: Finite Difference Schemes For Coupled Reaction-diffusion Mod...mentioning

confidence: 99%

Dynamical Behavior of Nonlinear Coupled Reaction-Diffusion Model: A Numerical Study Utilizing ADI and Staggered Grid Finite Volume Method in Matlab

Saqib,

Akhtar,

Hasnain

et al. 2024

IEEE Access

View full text Add to dashboard Cite

In this research, we present two Numerical algorithms for studying the dynamics of spatially extended coupled nonlinear reaction-diffusion model. The difference in this work is to model a system of reacting and diffusing chemicals, and how to predict the dynamics of ecologically relevant behavior, including chaos. The goal can be achieved by applying a proposed staggered grid finite volume method for solving the Reaction model, rigorously validating the numerical method and grid generation techniques against established results. Additionally, we integrate an Alternating Direction Implicit formulation to compare the efficiency and accuracy of the model's results. Dynamical system analysis, including linear stability analysis, is applied to comprehend the fundamental qualitative features of the inhibitor-activator system inherent in the coupled reaction-diffusion equations. Results, presented in tables and graphical representations, show that the schemes' high accuracy at fourth-order precision in space and second-order in time, with conditional stability. Numerical results showed that the proposed finite volume approach was exceptionally proficient and accurate for tackling the two-dimensional nonlinear coupled reactiondiffusion model. Overall, the dialog highlights the significance of the proposed staggered grid finite approach and calculations for fathoming coupled reaction-diffusion equations are widely studied in biological and chemical systems of nonlinear differential equations.

show abstract

General Decay Synchronization of State and Spatial Diffusion Coupled Delayed Memristive Neural Networks With Reaction-diffusion Terms

Huang,

Zhao

2024

Int. J. Control Autom. Syst.

View full text Add to dashboard Cite

Event-triggered control for coupled reaction–diffusion complex network systems with finite-time synchronization

Cited by 26 publications

References 36 publications

Event‐triggered synchronization for stochastic delayed neural networks: Passivity and passification case

Event‐triggered synchronization for stochastic delayed neural networks: Passivity and passification case

Dynamical Behavior of Nonlinear Coupled Reaction-Diffusion Model: A Numerical Study Utilizing ADI and Staggered Grid Finite Volume Method in Matlab

General Decay Synchronization of State and Spatial Diffusion Coupled Delayed Memristive Neural Networks With Reaction-diffusion Terms

Contact Info

Product

Resources

About