Clustering-based Identification of MIMO Piecewise Affine Systems (CROSBI ID 649138)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Hure, Nikola ; Vašak, Mario
engleski
Clustering-based Identification of MIMO Piecewise Affine Systems
PieceWise Affine (PWA) models are used to approximate general nonlinear dynamics with an arbitrary precision. PWA model can be employed for a constrained optimal controller synthesis, whereas the complexity of the controller is in a large part determined with a complexity of the model. Among the prominent methods for a PWA system identification is the clustering- based identification, which is originally designed for identification of systems with a Multiple-Input Single-Output (MISO) structure.When applied for the Multiple-Input Multiple-Output (MIMO) system identification, previously used clustering-based approach implied independent estimation of PWA maps for each of the outputs, whereas the MIMO PWA model was constructed by merging the polyhedral partitions and parameters of each MISO model. PWA model obtained with the respective approach often contained a significant number of submodels, thus aggravating the controller design process. In this paper we propose a multivariate linear regression approach for the identification of a MIMO PWA model based on the clustering technique. The presented approach is a systematic extension and fully exploits all benefits of the clustering- based identification. The proposed approach is validated on a coupled MIMO system identification problem.
Identifikacija zasnovana na uskupljavanju, MIMO sustavi, po dijelovima afini modeli, K-means++ algoritam
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Podaci o prilogu
404-409.
2017.
objavljeno
10.1109/PC.2017.7976248
Podaci o matičnoj publikaciji
Proceedings of the 21st International Conference on Process Control
Podaci o skupu
21st International Conference on Process Control
predavanje
06.06.2017-09.06.2017
Štrbské Pleso, Slovačka