Finite-time parameter estimation without persistence of excitation - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2019

Finite-time parameter estimation without persistence of excitation

Jian Wang
  • Function : Author
  • PersonId : 737345
  • IdHAL : jian-wang

Abstract

The problem of adaptive estimation of constant parameters in the linear regressor model is studied without the hypothesis that regressor is Persistently Excited (PE). First, the initial vector estimation problem is transformed to a series of the scalar ones using the method of Dynamic Regressor Extension and Mixing (DREM). Second, several adaptive estimation algorithms are proposed for the scalar scenario. In such a case, if the regressor may be nullified asymptotically or in a finite time, then the problem of estimation is also posed on a finite interval of time. The efficiency of the proposed algorithms is demonstrated in numeric experiments for an academic example.

Domains

Automatic
Fichier principal
Vignette du fichier
ECC19_0623_Wang_A.pdf (358.13 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02084983 , version 1 (30-03-2019)

Identifiers

  • HAL Id : hal-02084983 , version 1

Cite

Jian Wang, Denis Efimov, Alexey A. Bobtsov. Finite-time parameter estimation without persistence of excitation. ECC 2019 - European Control Conference, Jun 2019, Naples, Italy. ⟨hal-02084983⟩
107 View
259 Download

Share

Gmail Facebook X LinkedIn More