Robust global identification of linear parameter varying systems with generalised expectation–maximisation algorithm. Issue 7 (1st April 2015)

• Record Type: Journal Article
• Authors: Yang, Xianqiang; Lu, Yaojie; Yan, Zhibin
• Is Part Of: IET control theory & applications. Volume 9:Issue 7(2015)
• Page Start: 1103
• Page End: 1110
• Publication Date: 2015-04-01
• Subjects: linear systems -- expectation‐maximisation algorithm -- identification -- robust control -- Gaussian distribution; robust global identification -- linear parameter varying systems -- generalised expectation–maximisation algorithm -- LPV system -- input–output setting -- industrial process data -- process modelling -- robust LPV modelling problem -- GEM algorithm -- measurement noise -- student t‐distribution -- conventional Gaussian distribution -- degrees of freedom parameter; Control theory -- Periodicals; Automatic control -- Periodicals; 629.8312
• DOI: 10.1049/iet-cta.2014.0694
• Languages: English
• ISSNs: 1751-8644

Abstract

Abstract : 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.