Concurrent learning for convergence in adaptive control without persistency of excitation

We show that for an adaptive controller that uses recorded and instantaneous data concurrently for adaptation, a verifiable condition on linear independence of the recorded data is sufficient to guarantee exponential tracking error and parameter error convergence. This condition is found to be less...

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Bibliographic Details
Published in49th IEEE Conference on Decision and Control (CDC) pp. 3674 - 3679
Main Authors Chowdhary, Girish, Johnson, Eric
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2010
Subjects
Adaptation model
Adaptive control
Convergence
Equations
Mathematical model
Parameter estimation
Uncertainty
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Summary:We show that for an adaptive controller that uses recorded and instantaneous data concurrently for adaptation, a verifiable condition on linear independence of the recorded data is sufficient to guarantee exponential tracking error and parameter error convergence. This condition is found to be less restrictive and easier to monitor than a condition on persistently exciting exogenous input signal required by traditional adaptive laws that use only instantaneous data for adaptation.
ISBN:142447745X
9781424477456
ISSN:0191-2216
DOI:10.1109/CDC.2010.5717148