Synchronization Analysis for Nonlinear Complex Networks With Reaction-Diffusion Terms Using Fuzzy-Model-Based Approach
The objective of this article is to synthesize a state-feedback controller, which is designed based on a Takagi-Sugeno (T-S) fuzzy model of the variable-coefficient complex network with reaction-diffusion terms such that the closed-loop system is synchronized.
Technology Overview
By employing the FMB approach and Lyapunov stability theory, we derived two different kinds of criteria for synchronization. One is obtained by using inequality techniques of solving PDE, and the other by applying the properties of membership functions based on the first criteria. Comparing the two synchronization methods, the latter is more effective and convictive.
Applications & Benefits
The synchronization of a class variable-coefficient complex networks with reaction-diffusion terms is investigated. Applying the properties of membership functions based on the first criteria. Finally, a simulation example was given to demonstrate the merits of the proposed approach.
Abstract：
The synchronization of a class variable-coefficient complex networks with reaction-diffusion terms is investigated. The objective of this article is to synthesize a state-feedback controller, which is designed based on a Takagi-Sugeno (T-S) fuzzy model of the variable-coefficient complex network with reaction-diffusion terms such that the closed-loop system is synchronized. With the support of Green formula and some matrix inequality techniques, and utilizing the upper and lower bounds of the membership functions, membership-function-dependent (MFD) synchronization conditions guaranteeing the system's synchronization are obtained in the form of linear matrix inequalities (LMIs). A numerical example is presented to verify the analysis results and illustrate the effectiveness of the proposed synchronization conditions.
Synchronization Analysis for Nonlinear Complex Networks with Reaction-Diffusion Terms Using Fuzzy-Model-Based Approach
Author：Huang C., Zhang X., Lam H.-K., Tsai S.-H.
Year：2021
Source publication：IEEE Transactions on Fuzzy Systems Volume 29 Issue 6
Subfield Highest percentage：99%
Control and Systems Engineering (2020) #2/260