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

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.