{"title":"New globally Lipschitz no-equilibrium fractional order systems and control of hidden memory chaotic attractors","authors":"Bichitra Kumar Lenka, Ranjit Kumar Upadhyay","doi":"10.1016/j.jocs.2025.102603","DOIUrl":null,"url":null,"abstract":"<div><div>In continuous-time no-equilibrium nonlinear fractional order systems, bounded attractors are often termed as hidden memory attractors that can provide very complicated dynamics, and their localization seems crucial. In many engineering applications of interest, a famous problem deals with the identification of globally Lipschitz no-equilibrium nonlinear fractional order systems that can produce hidden memory chaotic attractors that remain unknown. We address two new no-equilibrium 3-state variables nonlinear fractional order systems; one is non-autonomous and another is autonomous, where both systems nonlinearity satisfy the global Lipschitz condition. It has been discovered that such a non-autonomous system gives rise to a globally Lipschitz hidden memory chaotic attractor when system orders <span><math><mrow><msub><mrow><mi>δ</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>998</mn><mo>,</mo><msub><mrow><mi>δ</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>997</mn><mo>,</mo><msub><mrow><mi>δ</mi></mrow><mrow><mn>3</mn></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>999</mn></mrow></math></span>, also when <span><math><mrow><msub><mrow><mi>δ</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>999</mn><mo>,</mo><msub><mrow><mi>δ</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>999</mn><mo>,</mo><msub><mrow><mi>δ</mi></mrow><mrow><mn>3</mn></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>999</mn></mrow></math></span>. The autonomous system produces a globally Lipschitz hidden memory chaotic attractor when system orders become <span><math><mrow><msub><mrow><mi>δ</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>997</mn></mrow></math></span>, <span><math><mrow><msub><mrow><mi>δ</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>998</mn></mrow></math></span>, <span><math><mrow><msub><mrow><mi>δ</mi></mrow><mrow><mn>3</mn></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>999</mn></mrow></math></span> as well as <span><math><mrow><msub><mrow><mi>δ</mi></mrow><mrow><mn>1</mn></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>997</mn></mrow></math></span>, <span><math><mrow><msub><mrow><mi>δ</mi></mrow><mrow><mn>2</mn></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>997</mn></mrow></math></span>, <span><math><mrow><msub><mrow><mi>δ</mi></mrow><mrow><mn>3</mn></mrow></msub><mo>=</mo><mn>0</mn><mo>.</mo><mn>997</mn></mrow></math></span>. In many applications of interest, it is often needed to have globally Lipschitz hidden memory chaotic attractors and reference control goal dynamics that seem crucial to widen the use of important nonlinear systems. We introduce a novel control strategy to address controlling hidden memory chaotic attractors found in such systems that seem impossible to control in many control design problems. Numerical simulations, including theoretical analysis, illustrate the effectiveness of the proposed control method.</div></div>","PeriodicalId":48907,"journal":{"name":"Journal of Computational Science","volume":"88 ","pages":"Article 102603"},"PeriodicalIF":3.1000,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Science","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1877750325000808","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
In continuous-time no-equilibrium nonlinear fractional order systems, bounded attractors are often termed as hidden memory attractors that can provide very complicated dynamics, and their localization seems crucial. In many engineering applications of interest, a famous problem deals with the identification of globally Lipschitz no-equilibrium nonlinear fractional order systems that can produce hidden memory chaotic attractors that remain unknown. We address two new no-equilibrium 3-state variables nonlinear fractional order systems; one is non-autonomous and another is autonomous, where both systems nonlinearity satisfy the global Lipschitz condition. It has been discovered that such a non-autonomous system gives rise to a globally Lipschitz hidden memory chaotic attractor when system orders , also when . The autonomous system produces a globally Lipschitz hidden memory chaotic attractor when system orders become , , as well as , , . In many applications of interest, it is often needed to have globally Lipschitz hidden memory chaotic attractors and reference control goal dynamics that seem crucial to widen the use of important nonlinear systems. We introduce a novel control strategy to address controlling hidden memory chaotic attractors found in such systems that seem impossible to control in many control design problems. Numerical simulations, including theoretical analysis, illustrate the effectiveness of the proposed control method.
期刊介绍:
Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory.
The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation.
This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods.
Computational science typically unifies three distinct elements:
• Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous);
• Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems;
• Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).