A control-interval-dependent functional for exponential stabilization of neural networks via intermittent sampled-data control
作者：
Highlights：
• An ISC scheme is developed, which combines the advantages of sampled-data control and intermittent control.
• A control-interval-dependent functional is constructed for the stabilization analysis of the resulting closed-loop system.
• Based on the designed Lyapunov functional, the ‘jump’ phenomena of adjacent Lyapunov functionals at the switching instants can be eliminated and some unnecessary restrictions on the Lyapunov matrices are dropped.
• In combination with the Lyapunov stability theory and some inequality techniques, two stability criteria are presented to guarantee the exponential stabilization of NNs via ISC.
摘要
•An ISC scheme is developed, which combines the advantages of sampled-data control and intermittent control.•A control-interval-dependent functional is constructed for the stabilization analysis of the resulting closed-loop system.•Based on the designed Lyapunov functional, the ‘jump’ phenomena of adjacent Lyapunov functionals at the switching instants can be eliminated and some unnecessary restrictions on the Lyapunov matrices are dropped.•In combination with the Lyapunov stability theory and some inequality techniques, two stability criteria are presented to guarantee the exponential stabilization of NNs via ISC.
论文关键词：Control-interval-dependent Lyapunov functional,Exponential stabilization,Neural networks,Intermittent sampled-data control
论文评审过程：Received 17 January 2021, Revised 12 May 2021, Accepted 28 June 2021, Available online 12 July 2021, Version of Record 12 July 2021.
论文官网地址：https://doi.org/10.1016/j.amc.2021.126494
