Performance Enhancement of Parameter Estimators via Dynamic Regressor Extension and Mixing

TL;DR

This paper introduces a novel two-stage method for designing parameter estimators that improves convergence and performance by generating and mixing new regressors, applicable to both linear and nonlinear models.

Contribution

The paper presents a new estimator design technique using dynamic regressor extension and mixing, removing the need for persistency of excitation in linear regressions.

Findings

Enhanced convergence without persistency of excitation

Effective application to nonlinear regressions

Simulation results demonstrate improved performance

Abstract

A new way to design parameter estimators with enhanced performance is proposed in the paper. The procedure consists of two stages, first, the generation of new regression forms via the application of a dynamic operator to the original regression. Second, a suitable mix of these new regressors to obtain the final desired regression form. For classical linear regression forms the procedure 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.