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

被引：6

作者：

Wang, Xinyue ^{[1
]}

Ma, Junxia ^{[1
]}

Xiong, Weili ^{[1
]}

机构：

[1] Jiangnan Univ, Sch Internet Things Engn, Key Lab Adv Proc Control Light Ind, Minist Educ, Wuxi 214122, Peoples R China

来源：

INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS | 2023年 / 21卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Bilinear state-space system; expectation-maximization algorithm; Kalman smoother; missing outputs; parameter estimation; IDENTIFICATION; MODEL;

D O I：

10.1007/s12555-021-1029-5

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：912 / 923

页数：12

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