Bounded real lemma and H∞ control for BAM neural networks with unbounded time-varying delays

IF 3.2 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

International Journal of Robust and Nonlinear Control Pub Date : 2024-08-21 DOI:10.1002/rnc.7606

Zhuo Ren, Yu Xue, Tingting Yu

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引用次数: 0

Abstract

This article investigates the exponential $H_{\infty}$ control issue of bidirectional associative memory neural network (BAMNN) with unbounded time-varying delays. A bounded real lemma (BRL) is first established via a direct method, which is on the basis of the solutions of BAMNN. Second, based on the obtained BRL, the state feedback controller is designed to guarantee the global exponential stability of the resulting closed-loop BAMNN with an $H_{\infty}$ performance index. Since no Lyapunov–Krasovskii functionals is constructed in the proposed method, the computation burden and complexity are reduced. Lastly, the effectiveness of the theoretical results is illustrated through two numerical examples.

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具有无界时变延迟的 BAM 神经网络的有界实数困境和 H∞ 控制

本文研究了具有无界时变延迟的双向关联记忆神经网络（BAMNN）的指数控制问题。首先，在 BAMNN 解的基础上，通过直接法建立了有界实数 Lemma（BRL）。其次，根据所得到的有界实数 Lemma，设计状态反馈控制器，以保证闭环 BAMNN 的全局指数稳定性，并给出性能指标。由于提出的方法不需要构建 Lyapunov-Krasovskii 函数，因此减少了计算负担和复杂性。最后，通过两个数值示例说明了理论结果的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Robust and Nonlinear Control 工程技术-工程：电子与电气

CiteScore

6.70

自引率

20.50%

发文量

505

审稿时长

2.7 months

期刊介绍： Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.