Reinforcement learning-based funnel control and privacy preservation for multi-agent systems with input dead-zone
This paper investigates the privacy-preserving protocol and reinforcement learning-based funnel controller design of multi-agent systems subject to input dead-zone constraints. An adaptive funnel controller is formulated to guarantee that the tracking errors keep within prescribed boundaries. The un...
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| Vydáno v: | Neural networks Ročník 195; s. 108238 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
Elsevier Ltd
01.03.2026
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| ISSN: | 0893-6080, 1879-2782, 1879-2782 |
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| Abstract | This paper investigates the privacy-preserving protocol and reinforcement learning-based funnel controller design of multi-agent systems subject to input dead-zone constraints. An adaptive funnel controller is formulated to guarantee that the tracking errors keep within prescribed boundaries. The uncharacterized system nonlinearities are approximated by a fuzzy function embedded in an actor-critic reinforcement learning paradigm. To address input constraints and alleviate communication burden, an event-triggered scheme is introduced to update control signals efficiently. Additionally, a secure data-exchange mechanism in light of Paillier cryptographic scheme is designed to safeguard the privacy of state information during transmission. Two comprehensive simulations are performed to validate the feasibility and performance of the developed strategy. |
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| AbstractList | This paper investigates the privacy-preserving protocol and reinforcement learning-based funnel controller design of multi-agent systems subject to input dead-zone constraints. An adaptive funnel controller is formulated to guarantee that the tracking errors keep within prescribed boundaries. The uncharacterized system nonlinearities are approximated by a fuzzy function embedded in an actor-critic reinforcement learning paradigm. To address input constraints and alleviate communication burden, an event-triggered scheme is introduced to update control signals efficiently. Additionally, a secure data-exchange mechanism in light of Paillier cryptographic scheme is designed to safeguard the privacy of state information during transmission. Two comprehensive simulations are performed to validate the feasibility and performance of the developed strategy. This paper investigates the privacy-preserving protocol and reinforcement learning-based funnel controller design of multi-agent systems subject to input dead-zone constraints. An adaptive funnel controller is formulated to guarantee that the tracking errors keep within prescribed boundaries. The uncharacterized system nonlinearities are approximated by a fuzzy function embedded in an actor-critic reinforcement learning paradigm. To address input constraints and alleviate communication burden, an event-triggered scheme is introduced to update control signals efficiently. Additionally, a secure data-exchange mechanism in light of Paillier cryptographic scheme is designed to safeguard the privacy of state information during transmission. Two comprehensive simulations are performed to validate the feasibility and performance of the developed strategy.This paper investigates the privacy-preserving protocol and reinforcement learning-based funnel controller design of multi-agent systems subject to input dead-zone constraints. An adaptive funnel controller is formulated to guarantee that the tracking errors keep within prescribed boundaries. The uncharacterized system nonlinearities are approximated by a fuzzy function embedded in an actor-critic reinforcement learning paradigm. To address input constraints and alleviate communication burden, an event-triggered scheme is introduced to update control signals efficiently. Additionally, a secure data-exchange mechanism in light of Paillier cryptographic scheme is designed to safeguard the privacy of state information during transmission. Two comprehensive simulations are performed to validate the feasibility and performance of the developed strategy. |
| ArticleNumber | 108238 |
| Author | Liu, Xiaoyang Yu, Wenwu Huang, Jiaxin Shen, Sikai |
| Author_xml | – sequence: 1 givenname: Jiaxin surname: Huang fullname: Huang, Jiaxin organization: School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, 221116, Jiangsu, PR China – sequence: 2 givenname: Xiaoyang orcidid: 0000-0001-5215-8270 surname: Liu fullname: Liu, Xiaoyang email: xyliu@jsnu.edu.cn organization: School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, 221116, Jiangsu, PR China – sequence: 3 givenname: Sikai orcidid: 0009-0005-3810-5320 surname: Shen fullname: Shen, Sikai organization: School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, 221116, Jiangsu, PR China – sequence: 4 givenname: Wenwu surname: Yu fullname: Yu, Wenwu organization: School of Mathematics, Southeast University, Nanjing, 210096, Jiangsu, PR China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/41138440$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1016/j.sysconle.2020.104869 10.1109/TCYB.2022.3165074 10.1109/TCYB.2023.3270487 10.1016/j.automatica.2019.04.029 10.1016/j.neunet.2025.107288 10.1007/s40815-023-01664-1 10.1109/TNNLS.2021.3090570 10.1016/j.neucom.2017.02.030 10.1109/TCYB.2018.2875134 10.1016/j.ins.2024.120189 10.1109/TVT.2022.3232815 10.1109/JIOT.2024.3386768 10.1109/TCYB.2020.2985221 10.1016/j.neunet.2021.12.019 10.1109/TSMC.2020.2972395 10.1109/TAC.2019.2902731 10.1080/00207721.2014.981236 10.1109/TNNLS.2023.3241070 10.1016/j.ins.2022.09.008 10.1109/TCNS.2023.3274700 10.1109/TASE.2023.3289445 10.1016/j.ins.2025.122129 10.1109/TASE.2024.3458176 10.1109/TFUZZ.2023.3298385 10.1109/9.486648 10.1109/TASE.2024.3374242 10.1109/TCYB.2024.3519171 10.1109/72.159070 10.1109/TFUZZ.2024.3441008 10.1109/TCNS.2023.3338256 10.1109/TSMC.2022.3211658 10.1016/j.automatica.2021.109847 10.1109/TSMC.2015.2486751 10.1016/j.isatra.2021.07.048 |
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| Keywords | Input dead-zone Nonlinear multi-agent systems Privacy preservation Funnel control Event-triggered strategy Reinforcement learning |
| Language | English |
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| SubjectTerms | Event-triggered strategy Funnel control Input dead-zone Nonlinear multi-agent systems Privacy preservation Reinforcement learning |
| Title | Reinforcement learning-based funnel control and privacy preservation for multi-agent systems with input dead-zone |
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