Performance Enhancement of Parameter Estimators via Dynamic Regressor Extension and Mixing

A new procedure to design parameter estimators with enhanced performance is proposed in the technical note. For classical linear regression forms, it yields a new parameter estimator whose convergence is established without the usual requirement of regressor persistency of excitation. The technique...

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Bibliographic Details
Published inIEEE transactions on automatic control Vol. 62; no. 7; pp. 3546 - 3550
Main Authors Aranovskiy, Stanislav, Bobtsov, Alexey, Ortega, Romeo, Pyrkin, Anton
Format Journal Article
LanguageEnglish
Published IEEE 01.07.2017
Institute of Electrical and Electronics Engineers
Subjects
Convergence
Eigenvalues and eigenfunctions
Engineering Sciences
Estimation
Linear regression
Mathematical model
monotonicity
nonlinear regressor
Parameter estimation
persistent excitation
Transient analysis
monotonicity
persistent excitation
Estimation
nonlinear re- gressor
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Summary:A new procedure to design parameter estimators with enhanced performance is proposed in the technical note. For classical linear regression forms, it yields a new parameter estimator whose convergence is established without the usual requirement of regressor persistency of excitation. The technique is also applied to nonlinear regressions with "partially" monotonic parameter dependence-giving rise again to estimators with enhanced performance. Simulation results illustrate the advantages of the proposed procedure in both scenarios.
ISSN:0018-9286
1558-2523
DOI:10.1109/TAC.2016.2614889