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

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

Domains: Engineering Sciences [physics]

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