New results on parameter estimation via dynamic regressor extension and mixing: continuous and discrete-time cases
File(s)
Accepted version
Author(s)
Ortega, Romeo
Aranovskiy, Stanislav
Pyrkin, Anton A
Astolfi, Alessandro
Bobtsov, Alexey A
Type
Journal Article
Abstract
We present some new results on the dynamic regressor extension and mixing parameter estimators for linear regression models recently proposed in the literature. This technique has proven instrumental in the solution of several open problems in system identification and adaptive control. The new results include the following, first, a unified treatment of the continuous and the discrete-time cases; second, the proposal of two new extended regressor matrices, one which guarantees a quantifiable transient performance improvement, and the other exponential convergence under conditions that are strictly weaker than regressor persistence of excitation; and, third, an alternative estimator ensuring convergence in finite-time whose adaptation gain, in contrast with the existing one, does not converge to zero. Simulations that illustrate our results are also presented.
Date Issued
2020-06-19
Date Acceptance
2020-06-19
Citation
IEEE Transactions on Automatic Control, 2020, 66 (5), pp.2265-2272
ISSN
0018-9286
Publisher
Institute of Electrical and Electronics Engineers
Start Page
2265
End Page
2272
Journal / Book Title
IEEE Transactions on Automatic Control
Volume
66
Issue
5
Copyright Statement
© 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.
Identifier
http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000642765200026&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Science & Technology
Technology
Automation & Control Systems
Engineering, Electrical & Electronic
Engineering
Convergence
Transient analysis
Adaptive control
Mathematical model
Linear systems
Linear regression
Adaptation models
Adaptive systems
estimation
system identification
ADAPTIVE-CONTROL
SYSTEMS
IDENTIFICATION
OBSERVERS
Publication Status
Published
Date Publish Online
2020-06-19