Exponential H∞ stabilization of uncertain neural networks with time-varying delay and external disturbance via periodically intermittent control. (December 2022)

• Record Type: Journal Article
• Authors: Guo, Liangliang; Dong, Yali; Cong, Yuhao
• Is Part Of: Transactions of the Institute of Measurement and Control. Volume 44:Number 16(2022)
• Page Start: 3243
• Page End: 3254
• Publication Date: 2022-12
• Subjects: Exponential H∞ stabilization -- uncertain neural networks -- interval time-varying delay -- periodically intermittent control; Automatic control -- Periodicals; Measuring instruments -- Periodicals; Commande automatique -- Périodiques; Mesure -- Instruments -- Périodiques; 681.2
• DOI: 10.1177/01423312221100635
• Languages: English
• ISSNs: 0142-3312

Abstract

This paper is concerned with the exponentialH ∞ stabilization for a class of uncertain neural networks with interval time-varying delay and external disturbance via periodically intermittent control. By constructing a novel Lyapunov–Krasovskii functional (LKF) and applying some inequality techniques, delay-dependent sufficient conditions are derived to guarantee the exponentialH ∞ stabilization of the considered closed-loop system. These conditions are given in the form of linear matrix inequalities (LMIs). The intermittent state-feedback controller can reduce the effect of external disturbance to a prescribed attenuation levelγ . Furthermore, the desired controller gain matrix can be obtained by solving the obtained LMIs. Finally, numerical simulations are given to show the effectiveness and the benefits of the proposed method.