CCE Faculty Articles
Machine Learning Barycenter Approach to Identifying LPV State-Space Models
Document Type
Article
Publication Title
Proceedings of the American Control Conference
Event Date/Location
Boston, MA
ISSN
0743-1619
Publication Date
7-2016
Abstract
In this paper an identification method for state– space LPV models is presented. The method is based on a particular parameterization that can be written in linear regression form and enables model estimation to be handled using Least–Squares Support Vector Machine (LS–SVM). The regression form has a set of design variables that act as filter poles to the underlying basis functions. In order to preserve the meaning of the Kernel functions (crucial in the LS–SVM context), these are filtered by a 2D–system with the predictor dynamics. A data–driven, direct optimization based approach for tuning this filter is proposed. The method is assessed using a simulated example and the results obtained are twofold. First, in spite of the difficult nonlinearities involved, the nonparametric algorithm was able to learn the underlying dependencies on the scheduling signal. Second, a significant improvement in the performance of the proposed method is registered, if compared with the one achieved by placing the predictor poles at the origin of the complex plane, which is equivalent to considering an estimator based on an LPV auto–regressive structure.
First Page
6351
Last Page
6356
NSUWorks Citation
Ramos, Jose A.; Lopes dos Santos, Paulo; Romano, Rodrigo A.; Pait, Felipe; and Azevedo Perdicoulis, Teresa Paula, "Machine Learning Barycenter Approach to Identifying LPV State-Space Models" (2016). CCE Faculty Articles. 417.
https://nsuworks.nova.edu/gscis_facarticles/417
