access icon free Robust global identification of linear parameter varying systems with generalised expectation–maximisation algorithm

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.

Inspec keywords: robust control; identification; Gaussian distribution; linear systems; expectation-maximisation algorithm

Other keywords: GEM algorithm; input–output setting; degrees of freedom parameter; measurement noise; conventional Gaussian distribution; student t-distribution; generalised expectation–maximisation algorithm; industrial process data; process modelling; linear parameter varying systems; robust global identification; LPV system; robust LPV modelling problem

Subjects: Simulation, modelling and identification; Other topics in statistics; Stability in control theory

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http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cta.2014.0694
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