Performance Enhancement of Parameter Estimators via Dynamic Regressor Extension and Mixing*

Stanislav Aranovskiy 1 Alexey Bobtsov 2 Romeo Ortega 3 Anton Pyrkin 2
1 ITMO University [Russia]
2 Saint Petersburg State University of Information Technologies Mechanics and Optics
3 L2S - Laboratoire des signaux et systèmes
Abstract : 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.
Document type :
Journal articles
Complete list of metadatas
https://hal-centralesupelec.archives-ouvertes.fr/hal-01612256
Contributor : Stanislav Aranovskiy <>
Submitted on : Friday, October 6, 2017 - 4:01:10 PM
Last modification on : Thursday, April 5, 2018 - 12:30:06 PM

Links full text

arxiv

Identifiers

Collections

CENTRALESUPELEC | L2S | SUP_LSS | UNIV-PARIS-SACLAY | SUP_SYSTEMES

Citation

Stanislav Aranovskiy, Alexey Bobtsov, Romeo Ortega, Anton Pyrkin. Performance Enhancement of Parameter Estimators via Dynamic Regressor Extension and Mixing*. IEEE Transactions on Automatic Control, Institute of Electrical and Electronics Engineers, 2017, 62 (7), pp.3546 - 3550. ⟨10.1109/TAC.2016.2614889⟩. ⟨hal-01612256⟩

Export

BibTeX TEI DC DCterms EndNote

Share

Metrics

Record views

310

Contact

CCSD