Open AccessFiltering‐based iterative identification for multivariable systems
Author(s) -
Wang Yanjiao,
Ding Feng
Publication year - 2016
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.2015.1195
Subject(s) - multivariable calculus , autoregressive model , noise (video) , identification (biology) , algorithm , system identification , convergence (economics) , computer science , iterative method , process (computing) , control theory (sociology) , mathematics , artificial intelligence , data modeling , engineering , statistics , control engineering , control (management) , botany , biology , database , economics , image (mathematics) , economic growth , operating system
This study applies the filtering technique to system identification to study the data filtering‐based parameter estimation methods for multivariable systems, which are corrupted by correlated noise – an autoregressive moving average process. To solve the difficulty that the identification model contains the unmeasurable variables and noise terms in the information matrix, the authors present a hierarchical gradient‐based iterative (HGI) algorithm by using the hierarchical identification principle. To improve the convergence rate, they apply the filtering technique to derive a filtering‐based HGI algorithm and a filtering‐based hierarchical least squares‐based iterative (HLSI) algorithm. The simulation examples indicate that the filtering‐based HLSI algorithm has the highest computational efficiency among these three algorithms.