Open AccessParameter estimation for pseudo‐linear systems using the auxiliary model and the decomposition technique
Author(s) -
Ding Feng,
Wang Feifei,
Xu Ling,
Hayat Tasawar,
Alsaedi Ahmed
Publication year - 2017
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2016.0491
Subject(s) - recursive least squares filter , decomposition , identification (biology) , computation , interval (graph theory) , estimation theory , system identification , computer science , least squares function approximation , linear system , algorithm , control theory (sociology) , mathematics , mathematical optimization , data modeling , artificial intelligence , adaptive filter , statistics , ecology , mathematical analysis , control (management) , biology , botany , combinatorics , database , estimator
This study focuses on the parameter identification problems of pseudo‐linear systems. The main goal is to present recursive least squares (RLS) estimation methods based on the auxiliary model identification idea and the decomposition technique. First, an auxiliary model‐based RLS algorithm is given as a comparison. Second, to improve the computation efficiency, a decomposition‐based RLS algorithm is presented. Then for the system identification with missing data, an interval‐varying RLS algorithm is derived for estimating the system parameters. Furthermore, this study uses the decomposition technique to reduce the computational cost in the interval‐varying RLS algorithm and introduces the forgetting factors to track the time‐varying parameters. The simulation results show that the proposed algorithms can work well.