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

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 ca...

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Published in:	International journal of robust and nonlinear control Vol. 34; no. 13; pp. 8768 - 8784
Main Authors:	Liu, Zhuangyu, Zhao, Shunyi, Wan, Haiying, Luan, Xiaoli, Liu, Fei
Format:	Journal Article
Language:	English
Published:	Bognor Regis Wiley Subscription Services, Inc 10.09.2024
Subjects:	Algorithms fermentation process Maximization Maximum likelihood estimates Nonlinear systems Optimization particle filter (PF) recursive EM algorithm Recursive functions unknown inputs (UIs)
ISSN:	1049-8923, 1099-1239
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Abstract	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.
AbstractList	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.
Author	Liu, Zhuangyu Zhao, Shunyi Wan, Haiying Liu, Fei Luan, Xiaoli
Author_xml	– sequence: 1 givenname: Zhuangyu surname: Liu fullname: Liu, Zhuangyu organization: Jiangnan University – sequence: 2 givenname: Shunyi surname: Zhao fullname: Zhao, Shunyi organization: Jiangnan University – sequence: 3 givenname: Haiying surname: Wan fullname: Wan, Haiying organization: Jiangnan University – sequence: 4 givenname: Xiaoli surname: Luan fullname: Luan, Xiaoli email: xlluan@jiangnan.edu.cn organization: Jiangnan University – sequence: 5 givenname: Fei orcidid: 0000-0001-7160-2605 surname: Liu fullname: Liu, Fei organization: Jiangnan University
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SubjectTerms	Algorithms fermentation process Maximization Maximum likelihood estimates Nonlinear systems Optimization particle filter (PF) recursive EM algorithm Recursive functions unknown inputs (UIs)
Title	Online state and unknown inputs estimation for nonlinear systems with particle filter based recursive expectation‐maximization algorithm
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