Multiple models reduction approach using gap metric for control of uncertain systems
by Ali Zribi; Mohamed Chtourou; Mohamed Djemel
International Journal of Modelling, Identification and Control (IJMIC), Vol. 17, No. 2, 2012

Abstract: In this paper, an internal multiple model control (IMMC) based on linear model's library is introduced. This approach supposes the definition of a set of local linear models. However, it remains beset with several difficulties such as the determination of the local models base. A new approach that combines fuzzy c-means (FCM) clustering algorithm and gap metric able to find the optimal number of local models is presented. The fuzzy clustering is used to divide the dataset into a large number of clusters where a local linear model is associated for each cluster. Then the gap metric analysis is applied to analyse the relationships among candidate local models, resulting in a reduced local models set. Such decomposition is shown to result in a set of stable and parsimonious models which can be deployed for online control.

Online publication date: Wed, 17-Dec-2014

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