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.
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IEEE Transactions on Automatic Control, Institute of Electrical and Electronics Engineers, 2017, 62 (7), pp.3546 - 3550. 〈10.1109/TAC.2016.2614889〉
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https://hal-centralesupelec.archives-ouvertes.fr/hal-01612256
Contributeur : Stanislav Aranovskiy <>
Soumis le : vendredi 6 octobre 2017 - 16:01:10
Dernière modification le : jeudi 5 avril 2018 - 12:30:06

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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〉

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