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Collaborative System Identification via Parameter Consensus

Papusha, Ivan and Lavretsky, Eugene and Murray, Richard M. (2014) Collaborative System Identification via Parameter Consensus. In: 2014 American Control Conference. IEEE , Piscataway, NJ, pp. 13-19. ISBN 978-1-4799-3272-6.

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Classical schemes in system identification and adaptive control often rely on persistence of excitation to guarantee parameter convergence, which may be difficult to achieve with a single agent and a single input. Inspired by consensus systems, we extend classical parameter adaptation to the multi agent setting by combining an adaptive gradient law with consensus dynamics. The gradient law represents the main learning signal, while consensus dynamics attract each agent's parameter estimates toward those of its neighbors. We show that the resulting decentralized online parameter estimator can be used to identify the true parameters of all agents, even if no single agent employs a persistently exciting input.

Item Type:Book Section
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URLURL TypeDescription DOIArticle
Murray, Richard M.0000-0002-5785-7481
Additional Information:© 2014 AACC. We thank S. You, A. Swaminathan, and Y. Mo for helpful discussions. This work was supported by a Department of Defense NDSEG Fellowship, and TerraSwarm, one of six centers of STARnet, a Semiconductor Research Corporation program sponsored by MARCO and DARPA.
Funding AgencyGrant Number
National Defense Science and Engineering Graduate (NDSEG) FellowshipUNSPECIFIED
Microelectronics Advanced Research Corporation (MARCO)UNSPECIFIED
Defense Advanced Research Projects Agency (DARPA)UNSPECIFIED
Semiconductor Research CorporationUNSPECIFIED
Record Number:CaltechAUTHORS:20150320-093857002
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Official Citation:Papusha, I.; Lavretsky, E.; Murray, R.M., "Collaborative system identification via parameter consensus," American Control Conference (ACC), 2014 , vol., no., pp.13,19, 4-6 June 2014 doi: 10.1109/ACC.2014.6858938 URL:
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:55944
Deposited By: Tony Diaz
Deposited On:20 Mar 2015 16:44
Last Modified:10 Nov 2021 20:52

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