Extension of a clustering identification approach to multivariable piecewise affine systems: Application to an industrial dryer

The main objective of this paper is the extension of the clustering-based identification approach to Multi-Input Multi-Output (MIMO) PieceWise Affine systems (PWA). This approach is performed by three main steps which are data clustering, parameters matrices estimation and regions computing. Data clustering is the most important step because the performances depend on the results given by the used clustering algorithm. In the case of MIMO PWA systems, we should cluster matrices of parameters which are considered high dimensional data. However, most of the conventional clustering algorithms are not efficient since the similarity assessment which is based on the distances between objects is fruitless in high dimension space. Therefore, we propose an extension of the DBSCAN (Density Based Spatial Clustering of Applications with Noise) clustering technique to identify MIMO PWA systems. The simulation results presented in this paper illustrate the performance of the proposed method. An application to an industrial dryer of Di-Calcium Phosphate (DCP) is also presented in order to strengthen the simulation results.