Publication

Library-based adaptive observation through a sparsity-promoting adaptive observer

Conference Article

Conference

European Control Conference (ECC)

Edition

20th

Pages

2187-2192

Doc link

http://dx.doi.org/10.23919/ECC54610.2021.9655070

File

Download the digital copy of the doc pdf document

Abstract

This paper proposes an adaptive observer for a class of nonlinear system with linear parametrization. The main novelty of the technique is that the regressor vector is considered to be unknown. Instead, a library of candidate non-linear functions is implemented, which transforms the original parameter vector into a new one that is characterized by being sparse. In such problem, it is shown that standard adaptive observers cannot recover the original vector due to a lack of persistence of excitation. Instead, a parameter-adaptation with an implicit l1 regularization is implemented. It is shown that this new observer can recover the parameter vector under standard assumptions of sparse signal recovery. The results are validated in a numerical simulation.

Categories

control theory, observability.

Author keywords

Adaptive observer, Parameter estimation, Sparse

Scientific reference

A. Cecilia and R. Costa. Library-based adaptive observation through a sparsity-promoting adaptive observer, 20th European Control Conference, 2021, Rotterdam, Netherlands, pp. 2187-2192.