Fast fixed-time synchronization control analysis for a class of coupled delayed Cohen-Grossberg neural networks. Issue 4 (March 2022)

• Record Type: Journal Article
• Authors: Wang, Xin; Cao, Jinde; Yang, Bin; Chen, Fubing
• Is Part Of: Journal of the Franklin Institute. Volume 359:Issue 4(2022)
• Page Start: 1612
• Page End: 1639
• Publication Date: 2022-03
• Subjects: Science -- Periodicals; Technology -- Periodicals; Patents -- United States -- Periodicals; 505
• DOI: 10.1016/j.jfranklin.2022.01.026
• Languages: English
• ISSNs: 0016-0032

Abstract

Abstract: In a fixed-time control system, the convergence rate and the fixed settling time are two important performance indexes. In this paper, a novel fixed-time control law is proposed and designed to control a class of coupled delayed Cohen-Grossberg neural networks to achieve synchronization with fast convergence rate within a fixed settling time. It should be emphasized that the derived settling time approach can provide a tighter settling time to more effectively reflect the performance for fast convergence rate of the considered controlled system. Moreover, to show the advantages of the proposed fixed-time control law and the derived fixed settling time approach, the existing related control laws and fixed settling time approaches are further discussed. In addition, the obtained fixed-time synchronization control theory is applied to a secure communication scenario, which further shows the feasibility and innovation of the addressed theoretical results.