Convergent adaptive control based prescribed-time synchronization of switched fuzzy competitive network systems with time-varying delays
This paper addresses the prescribed-time control problem for discontinuous fuzzy neutral-type competitive neural networks (FNTCNNs) featuring switchings and time-varying delays. Notably, FNTCNNs constitute a generalized class of singularly perturbed Filippov systems. The establishment of a prescribe...
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| Published in | Neural networks Vol. 190; p. 107691 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Ltd
01.10.2025
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0893-6080 1879-2782 1879-2782 |
| DOI | 10.1016/j.neunet.2025.107691 |
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| Abstract | This paper addresses the prescribed-time control problem for discontinuous fuzzy neutral-type competitive neural networks (FNTCNNs) featuring switchings and time-varying delays. Notably, FNTCNNs constitute a generalized class of singularly perturbed Filippov systems. The establishment of a prescribed-time stability lemma for time-varying delay singularly perturbed systems remains a critical yet unresolved challenge. To address this, we first develop a novel prescribed-time stability lemma for singularly perturbed Filippov systems using adjustment functions, the comparison principle, and inequality techniques. This is achieved through the application of the one-norm and the introduction of a new stability definition for such systems. Considering the switching law inherent in FNTCNNs, we achieve prescribed-time stabilization control by designing adaptive prescribed-time control strategies, employing differential inclusion theory and Filippov’s solution framework. The proposed adaptive control strategies demonstrate convergence properties, ensuring that both the control strategies and system state variables converge to zero within the same prescribed-time interval. These newly developed strategies offer significant advantages over existing approaches. Finally, we validate our principal results through numerical simulations of second-order multi-agent systems subject to discontinuous disturbances. |
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| AbstractList | This paper addresses the prescribed-time control problem for discontinuous fuzzy neutral-type competitive neural networks (FNTCNNs) featuring switchings and time-varying delays. Notably, FNTCNNs constitute a generalized class of singularly perturbed Filippov systems. The establishment of a prescribed-time stability lemma for time-varying delay singularly perturbed systems remains a critical yet unresolved challenge. To address this, we first develop a novel prescribed-time stability lemma for singularly perturbed Filippov systems using adjustment functions, the comparison principle, and inequality techniques. This is achieved through the application of the one-norm and the introduction of a new stability definition for such systems. Considering the switching law inherent in FNTCNNs, we achieve prescribed-time stabilization control by designing adaptive prescribed-time control strategies, employing differential inclusion theory and Filippov’s solution framework. The proposed adaptive control strategies demonstrate convergence properties, ensuring that both the control strategies and system state variables converge to zero within the same prescribed-time interval. These newly developed strategies offer significant advantages over existing approaches. Finally, we validate our principal results through numerical simulations of second-order multi-agent systems subject to discontinuous disturbances. This paper addresses the prescribed-time control problem for discontinuous fuzzy neutral-type competitive neural networks (FNTCNNs) featuring switchings and time-varying delays. Notably, FNTCNNs constitute a generalized class of singularly perturbed Filippov systems. The establishment of a prescribed-time stability lemma for time-varying delay singularly perturbed systems remains a critical yet unresolved challenge. To address this, we first develop a novel prescribed-time stability lemma for singularly perturbed Filippov systems using adjustment functions, the comparison principle, and inequality techniques. This is achieved through the application of the one-norm and the introduction of a new stability definition for such systems. Considering the switching law inherent in FNTCNNs, we achieve prescribed-time stabilization control by designing adaptive prescribed-time control strategies, employing differential inclusion theory and Filippov's solution framework. The proposed adaptive control strategies demonstrate convergence properties, ensuring that both the control strategies and system state variables converge to zero within the same prescribed-time interval. These newly developed strategies offer significant advantages over existing approaches. Finally, we validate our principal results through numerical simulations of second-order multi-agent systems subject to discontinuous disturbances.This paper addresses the prescribed-time control problem for discontinuous fuzzy neutral-type competitive neural networks (FNTCNNs) featuring switchings and time-varying delays. Notably, FNTCNNs constitute a generalized class of singularly perturbed Filippov systems. The establishment of a prescribed-time stability lemma for time-varying delay singularly perturbed systems remains a critical yet unresolved challenge. To address this, we first develop a novel prescribed-time stability lemma for singularly perturbed Filippov systems using adjustment functions, the comparison principle, and inequality techniques. This is achieved through the application of the one-norm and the introduction of a new stability definition for such systems. Considering the switching law inherent in FNTCNNs, we achieve prescribed-time stabilization control by designing adaptive prescribed-time control strategies, employing differential inclusion theory and Filippov's solution framework. The proposed adaptive control strategies demonstrate convergence properties, ensuring that both the control strategies and system state variables converge to zero within the same prescribed-time interval. These newly developed strategies offer significant advantages over existing approaches. Finally, we validate our principal results through numerical simulations of second-order multi-agent systems subject to discontinuous disturbances. |
| ArticleNumber | 107691 |
| Author | Gao, Dongdong Huang, Tingwen Kong, Fanchao |
| Author_xml | – sequence: 1 givenname: Dongdong surname: Gao fullname: Gao, Dongdong email: 225410@tlu.edu.cn organization: College of Mathematics and Computer Science, Tongling University, Tongling, Anhui 244000, China – sequence: 2 givenname: Fanchao surname: Kong fullname: Kong, Fanchao email: fanchaokong88@ahnu.edu.cn organization: School of Mathematics and Statistics, Anhui Normal University, Wuhu, Anhui 241000, China – sequence: 3 givenname: Tingwen orcidid: 0000-0001-9610-846X surname: Huang fullname: Huang, Tingwen email: huangtingwen@suat-sz.edu.cn organization: Faculty of Computer Science and Control Engineering, Shenzhen University of Advanced Technology, Shenzhen 518055, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40532522$$D View this record in MEDLINE/PubMed |
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| Keywords | Prescribed-time control Singularly perturbed Filippov systems Competitive neural networks Convergent control Adaptive control |
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| Snippet | This paper addresses the prescribed-time control problem for discontinuous fuzzy neutral-type competitive neural networks (FNTCNNs) featuring switchings and... |
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| StartPage | 107691 |
| SubjectTerms | Adaptive control Algorithms Competitive neural networks Computer Simulation Convergent control Fuzzy Logic Neural Networks, Computer Prescribed-time control Singularly perturbed Filippov systems Time Factors |
| Title | Convergent adaptive control based prescribed-time synchronization of switched fuzzy competitive network systems with time-varying delays |
| URI | https://dx.doi.org/10.1016/j.neunet.2025.107691 https://www.ncbi.nlm.nih.gov/pubmed/40532522 https://www.proquest.com/docview/3222356543 |
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