Synchronization in Networks of Nonlinear Systems: Contraction Analysis via Riemannian Metrics and Deep-Learning for Feedback Estimation
In this article, we consider the problem of exponential synchronization of a network of identical input-affine nonlinear time-varying systems connected through an undirected graph, in the presence of a leader. We tackle the problem with incremental stability tools. We propose sufficient metric-based...
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| Veröffentlicht in: | IEEE transactions on automatic control 2024-11, Vol.69 (11), p.8041-8048 |
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| container_title | IEEE transactions on automatic control |
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| creator | Giaccagli, Mattia Zoboli, Samuele Astolfi, Daniele Andrieu, Vincent Casadei, Giacomo |
| description | In this article, we consider the problem of exponential synchronization of a network of identical input-affine nonlinear time-varying systems connected through an undirected graph, in the presence of a leader. We tackle the problem with incremental stability tools. We propose sufficient metric-based conditions to design a distributed diffusive coupling feedback law in two frameworks. First, we consider a state feedback design, where synchronization is obtained for every initial condition. Then, we show that synchronization can still be achieved regionally under milder assumptions. To balance the analytical difficulties of computing the proposed controller, we develop an algorithm based on deep neural networks (DNNs) for practical implementation. |
| doi_str_mv | 10.1109/TAC.2024.3407015 |
| format | Article |
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(IEEE) 2024</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c209t-4c76253aa6a354d7bf5ddf7cc4e2bf863fb0f2c1a003d7907008e5ae66a382783</cites><orcidid>0000-0003-1877-6205 ; 0000-0002-6597-1137 ; 0009-0006-9225-4551 ; 0000-0003-3073-6900 ; 0000-0003-2690-7420</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/10541048$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>230,314,776,780,792,881,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/10541048$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://hal.science/hal-03801100$$DView record in HAL$$Hfree_for_read</backlink></links><search><creatorcontrib>Giaccagli, Mattia</creatorcontrib><creatorcontrib>Zoboli, Samuele</creatorcontrib><creatorcontrib>Astolfi, Daniele</creatorcontrib><creatorcontrib>Andrieu, Vincent</creatorcontrib><creatorcontrib>Casadei, Giacomo</creatorcontrib><title>Synchronization in Networks of Nonlinear Systems: Contraction Analysis via Riemannian Metrics and Deep-Learning for Feedback Estimation</title><title>IEEE transactions on automatic control</title><addtitle>TAC</addtitle><description>In this article, we consider the problem of exponential synchronization of a network of identical input-affine nonlinear time-varying systems connected through an undirected graph, in the presence of a leader. 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| subjects | Algorithms Artificial neural networks Computer Science Contraction Couplings deep learning deep neural network (DNN) incremental stability Machine learning Measurement multiagent systems Nonlinear systems State feedback Synchronism Synchronization Systems and Control Time synchronization Time varying control systems Trajectory Vectors |
| title | Synchronization in Networks of Nonlinear Systems: Contraction Analysis via Riemannian Metrics and Deep-Learning for Feedback Estimation |
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