Unscented Transformation Based Robust Kalman Filter and Its Applications in Fermentation Process

›› 2010, Vol. 18 ›› Issue (3): 412-418.

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Unscented Transformation Based Robust Kalman Filter and Its Applications in Fermentation Process

WANG Jianlin, FENG Xuying, ZHAO Liqiang, YU Tao

College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

Received:2009-09-25 Revised:2010-01-06 Online:2010-06-28 Published:2010-06-28
Supported by:
Supported by the National Natural Science Foundation of China(20476007,20676013)

Unscented Transformation Based Robust Kalman Filter and Its Applications in Fermentation Process

王建林, 冯絮影, 赵利强, 于涛

College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

通讯作者: WANG Jianlin,E-mail:wangjl@mail.buct.edu.cn
基金资助:
Supported by the National Natural Science Foundation of China(20476007,20676013)

Abstract

Abstract: State estimation is the precondition and foundation of a bioprocess monitoring and optimal control. However,there are many difficulties in dealing with a non-linear system,such as the instability of process, un-modeled dynamics,parameter sensitivity,etc.This paper discusses the principles and characteristics of three different approaches,extended Kalman filters,strong tracking filters and unscented transformation based Kalman filters.By introducing the unscented transformation method and a sub-optimal fading factor to correct the prediction error covariance,an improved Kalman filter,unscented transformation based robust Kalman filter,is proposed. The performance of the algorithm is compared with the strong tracking filter and unscented transformation based Kalman filter and illustrated in a typical case study for glutathione fermentation process.The results show that the proposed algorithm presents better accuracy and stability on the state estimation in numerical calculations.

Key words: robust Kalman filter, unscented transformation, fermentation process, nonlinear system

摘要： State estimation is the precondition and foundation of a bioprocess monitoring and optimal control. However,there are many difficulties in dealing with a non-linear system,such as the instability of process, un-modeled dynamics,parameter sensitivity,etc.This paper discusses the principles and characteristics of three different approaches,extended Kalman filters,strong tracking filters and unscented transformation based Kalman filters.By introducing the unscented transformation method and a sub-optimal fading factor to correct the prediction error covariance,an improved Kalman filter,unscented transformation based robust Kalman filter,is proposed. The performance of the algorithm is compared with the strong tracking filter and unscented transformation based Kalman filter and illustrated in a typical case study for glutathione fermentation process.The results show that the proposed algorithm presents better accuracy and stability on the state estimation in numerical calculations.

关键词: robust Kalman filter, unscented transformation, fermentation process, nonlinear system

WANG Jianlin, FENG Xuying, ZHAO Liqiang, YU Tao. Unscented Transformation Based Robust Kalman Filter and Its Applications in Fermentation Process[J]. , 2010, 18(3): 412-418.

王建林, 冯絮影, 赵利强, 于涛. Unscented Transformation Based Robust Kalman Filter and Its Applications in Fermentation Process[J]. , 2010, 18(3): 412-418.

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Unscented Transformation Based Robust Kalman Filter and Its Applications in Fermentation Process

Unscented Transformation Based Robust Kalman Filter and Its Applications in Fermentation Process

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