Recursive parameter identification of the dynamical models for bilinear state space systems

被引：0
|
作者：
Xiao Zhang
Feng Ding
Fuad E. Alsaadi
Tasawar Hayat
机构：
[1] Jiangnan University,Key Laboratory of Advanced Process Control for Light Industry (Ministry of Education)
[2] Qingdao University of Science and Technology,College of Automation and Electronic Engineering
[3] King Abdulaziz University,Department of Electrical and Computer Engineering, Faculty of Engineering
[4] Quaid-I-Azam University,Department of Mathematics
来源：
Nonlinear Dynamics | 2017年 / 89卷
关键词：
Dynamical system; Parameter estimation; State estimation; Multi-innovation theory; State space model; Bilinear system;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This paper investigates the recursive parameter and state estimation algorithms for a special class of nonlinear systems (i.e., bilinear state space systems). A state observer-based stochastic gradient (O-SG) algorithm is presented for the bilinear state space systems by using the gradient search. In order to improve the parameter estimation accuracy and the convergence rate of the O-SG algorithm, a state observer-based multi-innovation stochastic gradient algorithm and a state observer-based recursive least squares identification algorithm are derived by means of the multi-innovation theory. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed algorithms.
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页码：2415 / 2429
页数：14
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