Browse wiki

From MurrayWiki
Jump to: navigation, search
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 agent’s 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.  +
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.
15 May 2016 06:15:09  +
URL
This property is a special property in this wiki.
http://www.cds.caltech.edu/~murray/preprints/plm14-acc.pdf  +
show properties that link here 

 

Enter the name of the page to start browsing from.