| 不敏变换鲁棒Kalman滤波器在发酵过程中的应用(英文) |
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| 引用本文： | 王建林,冯絮影,赵利强,于涛.不敏变换鲁棒Kalman滤波器在发酵过程中的应用(英文)[J].中国化学工程学报,2010,18(3):412-418. |
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| 作者姓名： | 王建林 冯絮影 赵利强 于涛 |
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| 作者单位： | College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China |
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| 基金项目： | Supported by the National Natural Science Foundation of China(20476007,20676013) |
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| 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.
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| 关 键 词： | robust Kalman filter unscented transformation fermentation process nonlinear system |
| 收稿时间： | 2009-9-25 |
| 修稿时间： | 2009-9-25
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Unscented transformation based robust Kalman filter and its applications in fermentation process |
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| WANG Jianlin,FENG Xuying,ZHAO Liqiang,YU Tao.Unscented transformation based robust Kalman filter and its applications in fermentation process[J].Chinese Journal of Chemical Engineering,2010,18(3):412-418. |
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| Authors: | WANG Jianlin FENG Xuying ZHAO Liqiang YU Tao |
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| Affiliation: | College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China |
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| 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. |
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| Keywords: | robust Kalman filter unscented transformation fermentation process nonlinear system |
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