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

By S. Aranovskiy, A. Bobtsov, Romeo Ortega, A. Pyrkin. IEEE Transactions on Automatic Control, 2015

327 citations recorded by the source corpus.

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.

Subfields: Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4fc8b2a143bfa8db207e352a193da518ee27bd0f