Event‐triggered H∞$$ {H}_{\infty } $$ state estimation for neural networks with time‐varying delays: The continuous‐time case. (16th April 2022)

• Record Type: Journal Article
• Authors: Tian, Yufeng; Wang, Yuzhong; Ren, Junchao
• Is Part Of: International journal of robust and nonlinear control. Volume 32:Number 11(2022)
• Page Start: 6282
• Page End: 6292
• Publication Date: 2022-04-16
• Subjects: continuous‐time system -- event‐triggered H∞$$ {H}_{\infty } $$ state estimation -- integral inequality -- neural networks; Automatic control -- Periodicals; Control theory -- Periodicals; Nonlinear systems -- Periodicals; 629.836
• DOI: 10.1002/rnc.6150
• Languages: English
• ISSNs: 1049-8923

Abstract

Abstract: This article is concerned with the event‐triggered H ∞ $$ {H}_{\infty } $$ state estimation problem of delayed neural networks. A new event‐triggered scheme is designed based on the estimation error, which makes a balance between the state estimator performance and network communication bandwidth in accordance with practical requirement. An auxiliary function‐type free‐matrix‐based integral inequality is proposed, which can fully link the relationship among time‐varying delay and system states due to additional delay‐product‐type matrices. Based on these condiments, sufficient condition is established to guarantee the estimation error system to be asymptotically stable and satisfies the H ∞ $$ {H}_{\infty } $$ performance. Meanwhile, the desired event‐triggered estimator gains are derived in terms of linear matrix inequalities. A numerical example is illustrated to demonstrate the effectiveness of the obtained methods.