Artículo
Efficient and robust state estimation: Application to a copolymerization process
Fecha de publicación:
11/2020
Editorial:
John Wiley & Sons Inc.
Revista:
The Canadian Journal Of Chemical Engineering
ISSN:
0008-4034
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Polymerization processes are highly non-linear systems that require strict control of their dynamic operation to be competitive. The unscented Kalman filter is a filtering strategy that has shown a rewarding performance for non-linear state estimation. Besides, filters based on robust statistics have been proposed to deal with the presence of outliers. However, reported robust filters have employed only the Huber M-estimator as the loss function of the estimation problem. This work presents a new state-estimation procedure based on the unscented transformation and robust statistics concepts. When outliers are present, estimates are more accurate than when using the conventional filter. In contrast to previous research, our methodology is also efficient when there are no outliers. The performances of different loss functions for solving the estimation problem are presented. The results show that redescending M-estimators outperform the Huber function. The behaviour of the technique is analyzed for a copolymerization process.
Palabras clave:
POLYMERS
,
ROBUST STATISTICS
,
STATE ESTIMATION
,
UNSCENTED TRANSFORMATION
Archivos asociados
Licencia
Identificadores
Colecciones
Articulos(PLAPIQUI)
Articulos de PLANTA PILOTO DE INGENIERIA QUIMICA (I)
Articulos de PLANTA PILOTO DE INGENIERIA QUIMICA (I)
Citación
Tupaz Pantoja, Jhovany Alexander; Asteasuain, Mariano; Sanchez, Mabel Cristina; Efficient and robust state estimation: Application to a copolymerization process; John Wiley & Sons Inc.; The Canadian Journal Of Chemical Engineering; 99; S1; 11-2020; 1-29
Compartir
Altmétricas