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基于补偿控制器和切比雪夫神经网络的忆阻系统自适应控制

Adaptive control for memristive system via compensatory controller and Chebyshev neural network.

作者信息

Wang Shaofu

机构信息

School of Information and Management, Guangxi Medical University, Nanning, 530021, People's Republic of China.

出版信息

Sci Rep. 2024 Jun 9;14(1):13242. doi: 10.1038/s41598-024-61593-3.

Abstract

In this paper, based on linear matrix inequality technique, a simple controller and a compensatory controller are designed. It can track arbitrary fixed points and any periodic orbits. In addition, a synchronization control method via Chebyshev neural network with external disturbances is proposed. An adaptive controller is given. The Chebyshev neural network is used to approximate the uncertain nonlinear function and the adaptive law is used to adjust the corresponding parameters in the system. Taking a 4D memristive chaotic system as examples, the results are in consistent with the simulations. From a framework and control theoretical point of view, the proposed synchronization approach via compensation controller and Chebyshev neural network is firstly presented. From an application point of view, the proposed scheme can simplify the complexity of controller design. It is promising in many applications for mem-systems as secure communications and neural networks.

摘要

本文基于线性矩阵不等式技术,设计了一个简单控制器和一个补偿控制器。它能够跟踪任意固定点和任何周期轨道。此外,提出了一种具有外部干扰的基于切比雪夫神经网络的同步控制方法。给出了一个自适应控制器。切比雪夫神经网络用于逼近不确定非线性函数,自适应律用于调整系统中的相应参数。以一个4维忆阻混沌系统为例,结果与仿真一致。从框架和控制理论的角度来看,首次提出了通过补偿控制器和切比雪夫神经网络的同步方法。从应用的角度来看,所提出的方案可以简化控制器设计的复杂性。它在忆阻系统的许多应用中,如安全通信和神经网络,具有广阔的前景。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5fbc/11163013/dfa6e05318d7/41598_2024_61593_Fig1_HTML.jpg

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