CEC Faculty Articles

Title

Machine Learning Barycenter Approach to Identifying LPV State-Space Models

Event Date/Location

Boston, MA

Document Type

Article

Date

7-2016

Publication Title

Proceedings of the American Control Conference

ISSN or ISBN

0743-1619

First Page

6351

Last Page

6356

Description

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.

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