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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.
Keywords : Estimation persistent excitation nonlinear re- gressor monotonicity
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CNRS | CENTRALESUPELEC | L2S | SUP_SYSTEMES | SUP_LSS | GS-COMPUTER-SCIENCE | GS-ENGINEERING

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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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