TY - GEN
T1 - Weighted Recursive Least Square for Parameter Identification of Nonlinear Wiener–Hammerstein Systems
AU - Lan, Ruiguang
AU - Ren, Xuemei
AU - Li, Linwei
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - In this paper, in order to solve the problem that the controlled object is complex or nonlinear in many times, a weighted recursive least square scheme is used to estimate the parameters of the nonlinear Wiener-Hammerstein systems with the dead zone. First of all, we make the unknown dead zone linear by the switching operator and the intermediate function, and construct the parameter identification model of Wiener-Hammerstein system. Secondly, we obtain the parametric regression model of the concerned systems for parameter identification using the key-term separation principle. Thirdly, we build a fictitious auxiliary model to replace the immeasurable intermediate variable. And then, we estimate the parameters of the obtained model with the fictitious auxiliary model using the weighted recursive least square. Finally, we verify the feasibility of the algorithm by MATLAB simulation.
AB - In this paper, in order to solve the problem that the controlled object is complex or nonlinear in many times, a weighted recursive least square scheme is used to estimate the parameters of the nonlinear Wiener-Hammerstein systems with the dead zone. First of all, we make the unknown dead zone linear by the switching operator and the intermediate function, and construct the parameter identification model of Wiener-Hammerstein system. Secondly, we obtain the parametric regression model of the concerned systems for parameter identification using the key-term separation principle. Thirdly, we build a fictitious auxiliary model to replace the immeasurable intermediate variable. And then, we estimate the parameters of the obtained model with the fictitious auxiliary model using the weighted recursive least square. Finally, we verify the feasibility of the algorithm by MATLAB simulation.
KW - Parameter identification
KW - WRLS
KW - Wiener-hammerstein systems
UR - http://www.scopus.com/inward/record.url?scp=85117899207&partnerID=8YFLogxK
U2 - 10.1007/978-981-16-6328-4_47
DO - 10.1007/978-981-16-6328-4_47
M3 - Conference contribution
AN - SCOPUS:85117899207
SN - 9789811663277
T3 - Lecture Notes in Electrical Engineering
SP - 447
EP - 455
BT - Proceedings of 2021 Chinese Intelligent Systems Conference
A2 - Jia, Yingmin
A2 - Zhang, Weicun
A2 - Fu, Yongling
A2 - Yu, Zhiyuan
A2 - Zheng, Song
PB - Springer Science and Business Media Deutschland GmbH
T2 - 17th Chinese Intelligent Systems Conference, CISC 2021
Y2 - 16 October 2021 through 17 October 2021
ER -