Open AccessRobust global identification of linear parameter varying systems with generalised expectation–maximisation algorithm
Author(s) -
Yang Xianqiang,
Lu Yaojie,
Yan Zhibin
Publication year - 2015
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.2014.0694
Subject(s) - outlier , robustness (evolution) , algorithm , identification (biology) , gaussian , student's t distribution , mathematical optimization , system identification , mathematics , computer science , linear system , estimation theory , process (computing) , control theory (sociology) , artificial intelligence , data modeling , database , mathematical analysis , chemistry , biology , operating system , autoregressive conditional heteroskedasticity , biochemistry , quantum mechanics , control (management) , botany , econometrics , volatility (finance) , physics , gene
In this study, a robust approach to global identification of linear parameter varying (LPV) systems in an input–output setting is proposed. In practice, the industrial process data are often contaminated with outliers. In order to handle outliers in process modelling, the robust LPV modelling problem is formulated and solved in the scheme of generalised expectation–maximisation (GEM) algorithm. The measurement noise is taken to follow the Student's t ‐distribution instead of using the conventional Gaussian distribution, in this algorithm. The extent of robustness of the proposed approach is adaptively adjusted by optimising the degrees of freedom parameter of the Student's t ‐distribution iteratively through the maximisation step of the GEM algorithm. The numerical example is provided to demonstrate the effectiveness of the proposed approach.