Expectation-maximization Estimation Algorithm for Bilinear State-space Systems with Missing Outputs Using Kalman Smoother

In this paper, the parameter estimation of bilinear state-space systems with missing outputs is studied. The bilinear model is transformed into a linear time-varying state-space model, and Kalman smoother with a time-varying gain is adopted to estimate missing outputs and unmeasurable states. Under...

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Bibliographic Details
Published in:International journal of control, automation, and systems automation, and systems, 2023-03, Vol.21 (3), p.912-923
Main Authors: Wang, Xinyue, Ma, Junxia, Xiong, Weili
Format: Article
Language:English
Subjects:
Algorithms
Control
Engineering
Iterative methods
Maximization
Mechatronics
Optimization
Parameter estimation
Regular Papers
Robotics
State space models
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Summary:In this paper, the parameter estimation of bilinear state-space systems with missing outputs is studied. The bilinear model is transformed into a linear time-varying state-space model, and Kalman smoother with a time-varying gain is adopted to estimate missing outputs and unmeasurable states. Under the expectation-maximization (EM) algorithm scheme, an iterative estimation algorithm based on Kalman smoother is derived, in which the unknown parameters, missing outputs, and unmeasurable states can be estimated simultaneously. Two simulation examples, including a numerical example and a three-tank system experiment, are adopted to verify the effectiveness of the proposed algorithm.
ISSN:1598-6446
2005-4092
DOI:10.1007/s12555-021-1029-5