A decoupled data-driven strategy for estimating parameters with nonlinear dependence

Santiago D. Salas, Wilfredo Angulo, Dany De Cecchis

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1 Cita (Scopus)

Resumen

In this work, we introduce a decoupled data-driven methodology for the online estimation of parameters with nonlinear dependence. Common estimation methods for nonlinear systems rely on the incorporation of the mathematical model under study to the estimation structure. Other variational approaches trend to be more computationally expensive. In contrast, data-driven estimation methods, based on the retrospective cost model refinement (RCMR) algorithm, have demonstrated an efficient and adequate ability in the estimation of parameters. However, a better performance is observed when the estimation structure is divided into several independent sub-structures. The proposed decoupled RCMR strategy is explained and compared with the original structure using numerical examples. The results of the numerical examples show that the proposed strategy exhibits a good trade-off between the velocity of convergence and error minimization.

Idioma originalInglés
Título de la publicación alojadaProceedings - 2020 IEEE International Conference on Industrial Technology, ICIT 2020
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas17-22
Número de páginas6
ISBN (versión digital)9781728157542
DOI
EstadoPublicada - feb. 2020
Publicado de forma externa
Evento21st IEEE International Conference on Industrial Technology, ICIT 2020 - Buenos Aires, Argentina
Duración: 26 feb. 202028 feb. 2020

Serie de la publicación

NombreProceedings of the IEEE International Conference on Industrial Technology
Volumen2020-February

Conferencia

Conferencia21st IEEE International Conference on Industrial Technology, ICIT 2020
País/TerritorioArgentina
CiudadBuenos Aires
Período26/02/2028/02/20

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