针对致动器故障的延迟记忆双向关联记忆神经网络的非脆弱输出反馈控制

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-08-27 DOI:10.1016/j.amc.2024.129021
R. Suvetha , J.J. Nieto , P. Prakash
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引用次数: 0

摘要

本文研究了当从波动控制器接收到故障信号时,具有时变延迟的模型忆阻式双向关联记忆神经网络的稳定特性。本文考虑了非脆弱输出反馈控制器,以抵消增益扰动的影响,最终实现稳健的容错设置。为了解决从波动控制器中接收到的致动器中的微弱信号,控制增益矩阵包含了旨在记忆非脆弱输出反馈控制器的情况。基于 Lyapunov 稳定性理论、微分包容理论和全等变换,利用 Wirtinger 不等式,从线性矩阵不等式的角度获得了所设计的容错记忆双向关联记忆神经网络模型全局渐进稳定特性的充分条件。最后,利用所提出的忆阻双向关联记忆神经网络模型相对于时域平面的状态性能图进行数值示例,以证实稳定结果,并说明所设计控制器的工作机制。
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Non-fragile output-feedback control for delayed memristive bidirectional associative memory neural networks against actuator failure

This article investigates the stabilization property for the modeled memristive bidirectional associative memory neural networks with time-varying delay when the faulty signals received from the fluctuated controller. The non-fragile output-feedback controller is taken into account to counteract the impact of gain perturbations to end up with robust fault-tolerant setup. To tackle the weak signals in the actuator received from the fluctuated controller, control gain matrices encompass situations intended to memory non-fragile output-feedback controller. Based on the Lyapunov stability theory, differential inclusion theory, and congruence transformation, the sufficient condition for the global asymptotic stabilization property for the designed fault-tolerant memristive bidirectional associative memory neural network model is obtained in terms of linear matrix inequality by utilizing Wirtinger's inequality. Finally, numerical examples are approached with the state performance plots of the proposed memristive bidirectional associative memory neural network model with respect to the time-domain plane, to confirm the stabilization results and it illustrates the working mechanism of the designed controller.

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来源期刊
CiteScore
7.20
自引率
4.30%
发文量
567
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