A singular value maximizing data recording algorithm for concurrent learning

We present a singular value maximizing algorithm for recording data to be used by concurrent learning adaptive controllers. These controllers use recorded and current data concurrently and can have exponential stability guarantees, with the rate of convergence directly proportional to the minimum si...

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Bibliographic Details
Published inProceedings of the 2011 American Control Conference pp. 3547 - 3552
Main Authors Chowdhary, Girish, Johnson, Eric
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2011
Subjects
Adaptation models
Convergence
Data models
Equations
History
Mathematical model
Uncertainty
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ISBN1457700808
9781457700804
ISSN0743-1619
DOI10.1109/ACC.2011.5991481

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Summary:We present a singular value maximizing algorithm for recording data to be used by concurrent learning adaptive controllers. These controllers use recorded and current data concurrently and can have exponential stability guarantees, with the rate of convergence directly proportional to the minimum singular value of the matrix containing recorded data. The presented algorithm selects data for recording to improve the minimum singular value, and hence results in improved tracking performance, this is established through comparison with previously studied data recording methods that record points that are sufficiently different.
ISBN:1457700808
9781457700804
ISSN:0743-1619
DOI:10.1109/ACC.2011.5991481