A two-stage least squares based iterative parameter estimation algorithm for feedback nonlinear systems based on the model decomposition

Author

Peipei Hu ; Yongsong Xiao ; Rui Ding

Author_Institution

Key Lab. of Adv. Process Control for Light Ind. (Minist. of Educ.), Jiangnan Univ., Wuxi, China

fYear

2013

fDate

17-19 June 2013

Firstpage

5446

Lastpage

5450

Abstract

A two-stage least squares based iterative parameter estimation algorithm is proposed for identifying a feedback nonlinear system with the open-loop being a controlled autoregressive moving average model from input-output data. The identification model is bilinear on two unknown parameter vectors. By decomposing a system into two subsystems, we identify each subsystem, which is linear about a parameter vector. The simulation example is provided.

Keywords

autoregressive moving average processes; bilinear systems; feedback; iterative methods; least squares approximations; open loop systems; parameter estimation; vectors; autoregressive moving average model; bilinear identification model; feedback nonlinear system identification; input-output data; model decomposition; open-loop; parameter vectors; two-stage least squares based iterative parameter estimation algorithm; Autoregressive processes; Computational modeling; Iterative methods; Least squares approximations; Mathematical model; Nonlinear systems; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference (ACC), 2013

Conference_Location

Washington, DC

ISSN

0743-1619

Print_ISBN

978-1-4799-0177-7

Type

conf

DOI

10.1109/ACC.2013.6580689

Filename

6580689