{"title":"时变拓扑动态网络的同步拓扑估计与同步","authors":"Nana Wang, Esteban Restrepo, Dimos V. Dimarogonas","doi":"arxiv-2409.08404","DOIUrl":null,"url":null,"abstract":"We propose an adaptive control strategy for the simultaneous estimation of\ntopology and synchronization in complex dynamical networks with unknown,\ntime-varying topology. Our approach transforms the problem of time-varying\ntopology estimation into a problem of estimating the time-varying weights of a\ncomplete graph, utilizing an edge-agreement framework. We introduce two\nauxiliary networks: one that satisfies the persistent excitation condition to\nfacilitate topology estimation, while the other, a uniform-$\\delta$\npersistently exciting network, ensures the boundedness of both weight\nestimation and synchronization errors, assuming bounded time-varying weights\nand their derivatives. A relevant numerical example shows the efficiency of our\nmethods.","PeriodicalId":501315,"journal":{"name":"arXiv - CS - Multiagent Systems","volume":"15 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Simultaneous Topology Estimation and Synchronization of Dynamical Networks with Time-varying Topology\",\"authors\":\"Nana Wang, Esteban Restrepo, Dimos V. Dimarogonas\",\"doi\":\"arxiv-2409.08404\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose an adaptive control strategy for the simultaneous estimation of\\ntopology and synchronization in complex dynamical networks with unknown,\\ntime-varying topology. Our approach transforms the problem of time-varying\\ntopology estimation into a problem of estimating the time-varying weights of a\\ncomplete graph, utilizing an edge-agreement framework. We introduce two\\nauxiliary networks: one that satisfies the persistent excitation condition to\\nfacilitate topology estimation, while the other, a uniform-$\\\\delta$\\npersistently exciting network, ensures the boundedness of both weight\\nestimation and synchronization errors, assuming bounded time-varying weights\\nand their derivatives. A relevant numerical example shows the efficiency of our\\nmethods.\",\"PeriodicalId\":501315,\"journal\":{\"name\":\"arXiv - CS - Multiagent Systems\",\"volume\":\"15 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Multiagent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.08404\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Multiagent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.08404","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Simultaneous Topology Estimation and Synchronization of Dynamical Networks with Time-varying Topology
We propose an adaptive control strategy for the simultaneous estimation of
topology and synchronization in complex dynamical networks with unknown,
time-varying topology. Our approach transforms the problem of time-varying
topology estimation into a problem of estimating the time-varying weights of a
complete graph, utilizing an edge-agreement framework. We introduce two
auxiliary networks: one that satisfies the persistent excitation condition to
facilitate topology estimation, while the other, a uniform-$\delta$
persistently exciting network, ensures the boundedness of both weight
estimation and synchronization errors, assuming bounded time-varying weights
and their derivatives. A relevant numerical example shows the efficiency of our
methods.