State Estimation Approach by Fusing Delayed Measurements and Its Application

Authors

Pengcheng Qi, Key Laboratory of Advanced Process Control for Light Industry Ministry of Education, Jiangnan University, Wuxi 214122, China;
Zhonggai Zhao, Key Laboratory of Advanced Process Control for Light Industry Ministry of Education, Jiangnan University, Wuxi 214122, China;
Liu Fei, Key Laboratory of Advanced Process Control for Light Industry Ministry of Education, Jiangnan University, Wuxi 214122, China;

Abstract

Abstract: In batch processes, the key parameters were usually obtained online with low accuracy or offline with large time delay, and a state estimation algorithm was proposed to estimate the key parameters by incorporating delayed measurements with the real-time measurements. Due to the different sampling intervals of these two kinds of measurements, two cases were analyzed, including the case of only real-time measurements available and the case of both real-time and delayed measurements available. Considering the nature of nonlinearity and non-Gaussianity in batch processes, the particle filter algorithm was introduced for the state estimation, and it was further extended by the Bayesian method for the information fusion of these two kinds of measurements. Finally, the proposed method was applied in the beer fermentation process, and the experimental result shows that the proposed method performs well in the state estimation through incorporation of delayed measurements.

Recommended Citation

Qi, Pengcheng; Zhao, Zhonggai; and Fei, Liu (2020) "State Estimation Approach by Fusing Delayed Measurements and Its Application," Journal of System Simulation: Vol. 28: Iss. 8, Article 16.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss8/16

First Page

1818

Revised Date

2016-02-01

DOI Link

https://doi.org/

Last Page

1823

CLC

TN911.2

Recommended Citation

Qi Pengcheng, Zhao Zhonggai, Liu Fei. State Estimation Approach by Fusing Delayed Measurements and Its Application[J]. Journal of System Simulation, 2016, 28(8): 1818-1823.