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

作　　者：王建林 冯絮影 赵利强 于涛 

机构地区：[1]College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China

出　　处：《Chinese Journal of Chemical Engineering》2010年第3期412-418,共7页中国化学工程学报（英文版）

基　　金：Supported by the National Natural Science Foundation of China （20476007, 20676013）.

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.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 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TS262.5[自动化与计算机技术—控制科学与工程]