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基于自适应终端滑模控制的具有隐藏吸引子的哈密顿混沌系统的镇定

Stabilization of Port Hamiltonian Chaotic Systems with Hidden Attractors by Adaptive Terminal Sliding Mode Control.

作者信息

Azar Ahmad Taher, Serrano Fernando E

机构信息

Robotics and Internet-of-Things Lab (RIOTU), Prince Sultan University, Riyadh 12435, Saudi Arabia.

Faculty of Computers and Artificial Intelligence, Benha University, Benha 13511, Egypt.

出版信息

Entropy (Basel). 2020 Jan 19;22(1):122. doi: 10.3390/e22010122.

DOI:10.3390/e22010122
PMID:33285897
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7516430/
Abstract

In this study, the design of an adaptive terminal sliding mode controller for the stabilization of port Hamiltonian chaotic systems with hidden attractors is proposed. This study begins with the design methodology of a chaotic oscillator with a hidden attractor implementing the topological framework for its respective design. With this technique it is possible to design a 2-D chaotic oscillator, which is then converted into port-Hamiltonia to track and analyze these models for the stabilization of the hidden chaotic attractors created by this analysis. Adaptive terminal sliding mode controllers (ATSMC) are built when a Hamiltonian system has a chaotic behavior and a hidden attractor is detected. A Lyapunov approach is used to formulate the adaptive device controller by creating a control law and the adaptive law, which are used online to make the system states stable while at the same time suppressing its chaotic behavior. The empirical tests obtaining the discussion and conclusions of this thesis should verify the theoretical findings.

摘要

在本研究中，提出了一种用于稳定具有隐藏吸引子的端口哈密顿混沌系统的自适应终端滑模控制器的设计。本研究始于具有隐藏吸引子的混沌振荡器的设计方法，该方法为其各自的设计实现了拓扑框架。利用该技术可以设计一个二维混沌振荡器，然后将其转换为端口哈密顿系统，以跟踪和分析这些模型，从而稳定由此分析产生的隐藏混沌吸引子。当哈密顿系统具有混沌行为并检测到隐藏吸引子时，构建自适应终端滑模控制器（ATSMC）。采用李雅普诺夫方法，通过创建控制律和自适应律来制定自适应装置控制器，这些控制律和自适应律在线用于使系统状态稳定，同时抑制其混沌行为。获得本论文讨论和结论的实证测试应验证理论结果。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9d24/7516430/946ecbb5e4e4/entropy-22-00122-g012.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9d24/7516430/ef0ae9d26e18/entropy-22-00122-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9d24/7516430/17d12d12ece3/entropy-22-00122-g009.jpg
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