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

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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Contributor : Stanislav Aranovskiy <>
Submitted on : Friday, October 6, 2017 - 4:01:10 PM
Last modification on : Thursday, April 5, 2018 - 12:30:06 PM

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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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