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Conference papers

Finite-time parameter estimation without persistence of excitation

Jian Wang 1 Denis Efimov 2 Alexey Bobtsov 3
2 VALSE - Finite-time control and estimation for distributed systems
Inria Lille - Nord Europe, CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189
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.
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Submitted on : Saturday, March 30, 2019 - 9:40:25 AM
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  • HAL Id : hal-02084983, version 1


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



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