Extended Gradient-based Iterative Algorithm for Bilinear State-space Systems with Moving Average Noises by Using the Filtering Technique

被引：0
|
作者：
Siyu Liu
Yanliang Zhang
Ling Xu
Feng Ding
Ahmed Alsaedi
Tasawar Hayat
机构：
[1] Jiangnan University,Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education), School of Internet of Things Engineering
[2] Henan Polytechic University,School of Physics and Electronic Information Engineering
[3] Wuxi Vocational Institute of Commerce,School of Internet of Things Technology
[4] King Abdulaziz University,Department of Mathematics
来源：
International Journal of Control, Automation and Systems | 2021年 / 19卷
关键词：
Bilinear system; data filtering; iterative search; parameter estimation; state estimation;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper develops a filtering-based iterative algorithm for the combined parameter and state estimation problems of bilinear state-space systems, taking account of the moving average noise. In order to deal with the correlated noise and unknown states in the parameter estimation, a filter is chosen to filter the input-output data disturbed by colored noise and a Kalman state observer (KSO) is designed to estimate the states by minimizing the trace of the error covariance matrix. Then, a KSO extended gradient-based iterative (KSO-EGI) algorithm and a filtering based KSO-EGI algorithm are presented to estimate the unknown states and unknown parameters jointly by the iterative estimation idea. The simulation results demonstrate the effectiveness of the proposed algorithms.
引用
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页码：1597 / 1606
页数：9
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