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Online state and unknown inputs estimation for nonlinear systems with particle filter based recursive expectation-maximization algorithm
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DOI：10.1002/rnc.7416.png)
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Abstract
En 中文
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.
Keywords:
fermentation process
particle filter (PF)
recursive EM algorithm
unknown inputs (UIs)
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