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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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
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ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.7416