Open AccessIterative parameter estimation methods for dual‐rate sampled‐data bilinear systems by means of the data filtering technique
Author(s) -
Li Meihang,
Liu Ximei
Publication year - 2021
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/cth2.12118
Subject(s) - bilinear interpolation , dual (grammatical number) , iterative method , autoregressive model , algorithm , identification (biology) , system identification , estimation theory , computer science , bilinear transform , key (lock) , control theory (sociology) , noise (video) , mathematics , mathematical optimization , data modeling , filter (signal processing) , artificial intelligence , digital filter , statistics , control (management) , art , botany , literature , computer security , database , image (mathematics) , computer vision , biology
This paper considers the iterative parameter estimation for a dual‐rate sampled‐data bilinear system with autoregressive moving average noise. Through combining the auxiliary model identification idea with the data filtering technique, this paper derives two filtering auxiliary model gradient‐based iterative algorithms by using two different filters. The key is to construct an auxiliary model for predicting the unavailable outputs, and to transform the dual‐rate bilinear system identification model into two sub‐identification models. Finally, an auxiliary model gradient‐based iterative (AM‐GI) algorithm is presented for comparison. The simulation results indicate that the proposed algorithms are effective for identifying the dual‐rate sampled‐data bilinear systems, and can generate more accurate parameter estimates and have a higher computational efficiency than the AM‐GI algorithm.