Uniform Asymptotic Convergence of an Adaptive Algorithm With Diminishing Persistent Excitation

Soura Dasgupta; Bariş Fidan

doi:10.3182/20120711-3-BE-2027.00407

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Uniform Asymptotic Convergence of an Adaptive Algorithm With Diminishing Persistent Excitation

Journal article

Peer reviewed

Uniform Asymptotic Convergence of an Adaptive Algorithm With Diminishing Persistent Excitation

Soura Dasgupta and Bariş Fidan

IFAC Proceedings Volumes, Vol.45(16), pp.1514-1516

07/2012

DOI: 10.3182/20120711-3-BE-2027.00407

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Abstract

Conventional adaptive systems algorithms require persistent excitation (p.e.) for exponential convergence, in turn important for robustness. A recently proposed algorithm for localizing a target by a moving agent that can measure its distance from the target, reduces to the same error model. In this case persistent excitation requires the agent's velocity vector to be p.e.. Yet in many control tasks e.g. where an agent must dock on a target at an unknown location, by measuring its distance from the target, such a p.e. condition cannot be met. In this paper we propose a notion of an excitation condition where the degree of p.e. declines with the quality of estimate and prove uniform asymptotic convergence under this condition.

Adaptive Identification

Persistent Excitation

Uniform Asymptotic Convergence

Details

Title: Subtitle: Uniform Asymptotic Convergence of an Adaptive Algorithm With Diminishing Persistent Excitation
Creators: Soura Dasgupta - University of Iowa
Bariş Fidan - University of Waterloo
Resource Type: Journal article
Publication Details: IFAC Proceedings Volumes, Vol.45(16), pp.1514-1516
DOI: 10.3182/20120711-3-BE-2027.00407
ISSN: 1474-6670
Language: English
Date published: 07/2012
Academic Unit: Electrical and Computer Engineering
Record Identifier: 9984197342602771

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