Aperiodic Sampled-Data Control for Exponential Stabilization of Delayed Neural Networks: A Refined Two-Sided Looped-Functional Approach
Lan Yao, Zhen Wang, Xia Huang, Yuxia Li, Hao Shen, Guanrong Chen
Abstract
This brief addresses the exponential stabilization of a class of delayed neural networks under the framework of aperiodic sampled-data control. Firstly, a two-sided looped-functional is precisely constructed to relax the stabilization conditions and to enlarge the maximum sampling period. It drops the common positive definiteness requirement and only requires it at the sampling instants. Combining the Gronwall-Bellman inequality with the reciprocally convex approach, a less conservative exponential stabilization criterion in terms of LMIs with fewer decision variables is presented. Meanwhile, an effective design algorithm for the feedback gain matrix is proposed. Finally, a simulation example is provided to illustrate the effectiveness and superiority of the main results over some popular ones.