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Scalable δ‐Level Coherent State Synchronization of Multi‐Agent Systems with Adaptive Protocols and Bounded Disturbances.

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Title: Scalable δ‐Level Coherent State Synchronization of Multi‐Agent Systems with Adaptive Protocols and Bounded Disturbances.
Authors: Nojavanzadeh, Donya1 (AUTHOR) donya.nojavanzadeh@wsu.edu, Liu, Zhenwei2,3 (AUTHOR), Saberi, Ali1 (AUTHOR), Stoorvogel, Anton A.4 (AUTHOR)
Source: International Journal of Adaptive Control & Signal Processing. Mar2025, Vol. 39 Issue 3, p497-516. 20p.
Subjects: Coherent states, Communication models, Synchronization, Noise, Neighbors
Abstract: In this paper, we study scalable δ$$ \delta $$‐level coherent state synchronization for multi‐agent systems (MAS) where the agents are subject to bounded disturbances/noises. We propose a scale‐free framework designed solely based on the knowledge of agent models and agnostic to the communication graph and the size of the network. We define the level of coherency for each agent as the norm of the weighted sum of the disagreement dynamics with its neighbors. The objective is to restrict the network's coherency level to δ$$ \delta $$ without a‐priori information about the disturbance. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Adaptive Control & Signal Processing is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Scalable δ‐Level Coherent State Synchronization of Multi‐Agent Systems with Adaptive Protocols and Bounded Disturbances.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Adaptive+Control+%26+Signal+Processing%22">International Journal of Adaptive Control & Signal Processing</searchLink>. Mar2025, Vol. 39 Issue 3, p497-516. 20p.
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  Data: <searchLink fieldCode="DE" term="%22Coherent+states%22">Coherent states</searchLink><br /><searchLink fieldCode="DE" term="%22Communication+models%22">Communication models</searchLink><br /><searchLink fieldCode="DE" term="%22Synchronization%22">Synchronization</searchLink><br /><searchLink fieldCode="DE" term="%22Noise%22">Noise</searchLink><br /><searchLink fieldCode="DE" term="%22Neighbors%22">Neighbors</searchLink>
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  Data: In this paper, we study scalable δ$$ \delta $$‐level coherent state synchronization for multi‐agent systems (MAS) where the agents are subject to bounded disturbances/noises. We propose a scale‐free framework designed solely based on the knowledge of agent models and agnostic to the communication graph and the size of the network. We define the level of coherency for each agent as the norm of the weighted sum of the disagreement dynamics with its neighbors. The objective is to restrict the network's coherency level to δ$$ \delta $$ without a‐priori information about the disturbance. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Adaptive Control & Signal Processing is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Text: English
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      – SubjectFull: Communication models
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      – SubjectFull: Synchronization
        Type: general
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      – TitleFull: Scalable δ‐Level Coherent State Synchronization of Multi‐Agent Systems with Adaptive Protocols and Bounded Disturbances.
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              Text: Mar2025
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