.png){kind=icon}
.png){kind=icon}
Return
Online state and inputs identification for stochastic systems using recursive expectation-maximization algorithm
.png){kind=icon}
PRE
AI
DOI：10.1016/j.chemolab.2021.104403.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this paper, an online joint state estimation and unknown inputs (UIs) identification approach for industrial processes represented by the state-space model is proposed. The UIs identification is achieved by applying the recursive expectation-maximization (REM) technique. In E-step, a recursively calculated Q-function is derived based on the maximum likelihood framework, and the Kalman filter (KF) is adopted to estimate the states. In Mstep, analytical solutions for UIs are obtained via locally maximizing the recursive Q-function. A numerical example of a quadrupled water tank process and practice application to system modeling of a distillation tower are employed to illustrate the proposed REM-KF algorithm's effectiveness. It is also demonstrated that the REM-KF algorithm is more accurate than existing online solutions.
Keywords:
Unknown inputs (UIs) identification
State estimation
Recursive expectation-maximization (REM) al-gorithm
Kalman filter (KF)
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.8
Papers:
4.6K
Citations:
1.2W