Enhancing the settling time estimation of fixed-time stability and applying it to the predefined-time synchronization of delayed memristive neural networks with external unknown disturbance.


Journal

Chaos (Woodbury, N.Y.)
ISSN: 1089-7682
Titre abrégé: Chaos
Pays: United States
ID NLM: 100971574

Informations de publication

Date de publication:
Aug 2020
Historique:
entrez: 3 9 2020
pubmed: 3 9 2020
medline: 3 9 2020
Statut: ppublish

Résumé

This paper concentrates on the global predefined-time synchronization of delayed memristive neural networks with external unknown disturbance via an observer-based active control. First, a global predefined-time stability theorem based on a non-negative piecewise Lyapunov function is proposed, which can obtain more accurate upper bound of the settling time estimation. Subsequently, considering the delayed memristive neural networks with disturbance, a disturbance-observer is designed to approximate the external unknown disturbance in the response system with a Hurwitz theorem and then to eliminate the influence of the unknown disturbance. With the help of global predefined-time stability theorem, the predefined-time synchronization is achieved between two delayed memristive neural networks via an active control Lyapunov function design. Finally, two numerical simulations are performed, and the results are given to show the correctness and feasibility of the predefined-time stability theorem.

Identifiants

pubmed: 32872839
doi: 10.1063/5.0010145
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

083110

Auteurs

Lixiong Lin (L)

School of Mechanical Engineering and Automation, Fuzhou University, Fujian 350116, People's Republic of China.

Peixin Wu (P)

School of Mechanical Engineering and Automation, Fuzhou University, Fujian 350116, People's Republic of China.

Yanjie Chen (Y)

School of Mechanical Engineering and Automation, Fuzhou University, Fujian 350116, People's Republic of China.

Bingwei He (B)

School of Mechanical Engineering and Automation, Fuzhou University, Fujian 350116, People's Republic of China.

Classifications MeSH