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Efficient and robust state estimation: Application to a copolymerization process
Author(s) -
Tupaz Jhovany,
Asteasuain Mariano,
Sánchez Mabel
Publication year - 2021
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.23976
Subject(s) - outlier , kalman filter , estimator , robust statistics , transformation (genetics) , computer science , filter (signal processing) , process (computing) , state (computer science) , mathematical optimization , extended kalman filter , control theory (sociology) , mathematics , algorithm , statistics , artificial intelligence , control (management) , biochemistry , chemistry , computer vision , gene , operating system
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.