Online state and unknown inputs estimation for nonlinear systems with particle filter based recursive expectation-maximization algorithm

被引：2

作者：

Liu, Zhuangyu ^{[1
]}

Zhao, Shunyi ^{[1
]}

Wan, Haiying ^{[1
]}

Luan, Xiaoli ^{[1
]}

Liu, Fei ^{[1
]}

机构：

[1] Jiangnan Univ, Inst Automat, Key Lab Adv Proc Control Light Ind, Wuxi 214122, Peoples R China

来源：

INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL | 2024年 / 34卷 / 13期

基金：

中国国家自然科学基金;

关键词：

fermentation process; particle filter (PF); recursive EM algorithm; unknown inputs (UIs); IDENTIFICATION;

D O I：

10.1002/rnc.7416

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The article presents an innovative approach to simultaneously estimate states and unknown inputs (UIs) in nonlinear systems using a particle filter (PF) based recursive expectation-maximization (EM) algorithm. This method is distinct from traditional iterative EM algorithms. During the E-step, it calculates the Q-function recursively within the maximum likelihood framework, while the PF estimates the system states. The M-step involves local maximization of the recursive Q-function to online estimate the UIs. The effectiveness of the PF-based recursive EM algorithm is demonstrated with a numerical example, and comparisons with the augmented state PF are made to highlight its advantages. Finally, the proposed algorithm is implemented in a real application for the estimation of the continuous fermentation process.

引用

页码：8768 / 8784

页数：17

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