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Multiple models reduction approach using gap metric for control of uncertain systems
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.
Keywords
References
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