一种融合时滞值的状态估计方法及其应用

系统仿真学报 ›› 2016, Vol. 28 ›› Issue (8): 1818-1823.

一种融合时滞值的状态估计方法及其应用

祁鹏程, 赵忠盖, 刘飞

江南大学轻工过程先进控制教育部重点实验室,无锡 214122

收稿日期:2015-12-07 修回日期:2016-02-01 出版日期:2016-08-08 发布日期:2020-08-17
作者简介:祁鹏程(1991-),男,江苏扬州,硕士生,研究方向为间歇控制状态估计;赵忠盖(1976-),男,湖北荆州,博士,副教授,研究方向为间歇过程建模与软测量、工业系统监控与诊断。
基金资助:
江苏省六大人才高峰项目(2014-ZBZZ-010)

State Estimation Approach by Fusing Delayed Measurements and Its Application

Qi Pengcheng, Zhao Zhonggai, Liu Fei

Key Laboratory of Advanced Process Control for Light Industry Ministry of Education, Jiangnan University, Wuxi 214122, China

Received:2015-12-07 Revised:2016-02-01 Online:2016-08-08 Published:2020-08-17

摘要/Abstract

摘要： 针对间歇过程中关键参数,在线检测精度低、离线分析时滞大的问题,提出一种融合时滞测量值的状态估计方法。鉴于在线和离线检测值的采样周期不同,分仅有在线检测值和两种检测值并存等两种情况进行分析。考虑间歇过程的非线性、非高斯分布等特点,引入粒子滤波算法并基于贝叶斯方法对其进行扩展,实现两种检测值的信息融合。将提出的算法应用在啤酒发酵过程中,并与不考虑时滞测量值的估计效果对比。实验结果表明,该方法能够较好地处理考虑时滞值的状态估计问题,且效果优于不考虑时滞测量值的情况。

关键词: 间歇过程, 状态估计, 时滞测量值, 粒子滤波, 贝叶斯方法, 前/后向平滑

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.

Key words: batch fermentation, state estimation, delayed measurements, particle filtering, Bayesian method, forward-backward smoothing

中图分类号:

TN911.2

祁鹏程, 赵忠盖, 刘飞. 一种融合时滞值的状态估计方法及其应用[J]. 系统仿真学报, 2016, 28(8): 1818-1823.

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.

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