A new multistable double-scroll 4-D hyperchaotic system with no equilibrium point, its bifurcation analysis, synchronization and circuit design

IF 1.2 4区 计算机科学 Q4 AUTOMATION & CONTROL SYSTEMS Archives of Control Sciences Pub Date : 2023-07-20 DOI:10.24425/acs.2021.136882
S. Vaidyanathan, He Shaobo
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引用次数: 7

Abstract

In this work, we have developed a new 4-D dynamical system with hyperchaos and hidden attractor. First, by introducing a feedback input control into the 3-D Ma chaos system (2004), we obtain a new 4-D hyperchaos system with no equilibrium point. Thus, we derive a new hyperchaos system with hidden attractor. We carry out an extensive bifurcation analysis of the new hyperchaosmodel with respect to the three parameters.We also carry out probability density distribution analysis of the new hyperchaotic system. Interestingly, the new nonlinear hyperchaos system exhibits multistability with coexisting attractors. Next, we discuss global hyperchaos selfsynchronization for the new hyperchaos system via Integral SlidingMode Control (ISMC). As an engineering application, we realize the new 4-D hyperchaos system with an electronic circuit via MultiSim. The outputs of the MultiSim hyperchaos circuit show good match with the numerical MATLAB plots of the hyperchaos model. We also analyze the power spectral density (PSD) of the hyperchaos of the state variables using MultiSim.
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一种新的无平衡点多稳定双涡旋4维超混沌系统及其分岔分析、同步和电路设计
在这项工作中,我们开发了一个具有超混沌和隐藏吸引子的新的四维动力系统。首先,通过在三维Ma混沌系统中引入反馈输入控制(2004),我们得到了一个新的无平衡点的四维超混沌系统。由此,我们得到了一个新的具有隐藏吸引子的超混沌系统。我们就三个参数对新的超混沌模型进行了广泛的分岔分析。我们还对新的超混沌系统进行了概率密度分布分析。有趣的是,新的非线性超混沌系统具有吸引子共存的多重稳定性。接下来,我们讨论了利用积分滑模控制(Integral SlidingMode Control, ISMC)实现超混沌系统的全局自同步。作为工程应用,我们利用MultiSim软件实现了新的四维超混沌系统。MultiSim超混沌电路的输出与超混沌模型的MATLAB数值图吻合良好。利用MultiSim分析了状态变量超混沌的功率谱密度(PSD)。
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
Archives of Control Sciences
Archives of Control Sciences Mathematics-Modeling and Simulation
CiteScore
2.40
自引率
33.30%
发文量
0
审稿时长
14 weeks
期刊介绍: Archives of Control Sciences welcomes for consideration papers on topics of significance in broadly understood control science and related areas, including: basic control theory, optimal control, optimization methods, control of complex systems, mathematical modeling of dynamic and control systems, expert and decision support systems and diverse methods of knowledge modelling and representing uncertainty (by stochastic, set-valued, fuzzy or rough set methods, etc.), robotics and flexible manufacturing systems. Related areas that are covered include information technology, parallel and distributed computations, neural networks and mathematical biomedicine, mathematical economics, applied game theory, financial engineering, business informatics and other similar fields.
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