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Collaborative System Identification via Parameter Consensus |
Abstract |
Standard schemes in system identification … Standard schemes in system identification and adaptive control rely on persistence of excitation to guaran- tee parameter convergence. Inspired by networked systems, we extend parameter adaptation to the multi-agent setting by combining a gradient law with consensus dynamics. The gradient law introduces a learning signal, while consensus dynamics preferentially push each agentâs parameter estimates toward those of its neighbors. We show that the resulting online, decentralized parameter estimator combines local and neighboring information to identify the true parameters even if no single agent employs a persistently exciting input. We also elaborate upon collective persistence of excitation in networked adaptive algorithms. citation in networked adaptive algorithms. +
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Authors | Ivan Papusha, Eugene Lavretsky and Richard M. Murray + |
Funding | The TerraSwarm Research Center + |
ID | 2013j + |
Source | 2014 American Control Conference (ACC) + |
Tag | plm14-acc + |
Title | Collaborative System Identification via Parameter Consensus + |
Type | Conference Paper + |
Categories | Papers |
Modification date This property is a special property in this wiki.
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15 May 2016 06:15:09 + |
URL This property is a special property in this wiki.
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http://www.cds.caltech.edu/~murray/preprints/plm14-acc.pdf + |
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Collaborative System Identification via Parameter Consensus + | Title |
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