Regularized Moving-Horizon PWA Regression for LPV System Identification

摘要

This paper addresses the identification of Linear Parameter-Varying (LPV) models through regularized moving-horizon PieceWise Affine (PWA) regression. Specifically, the scheduling-variable space is partitioned into polyhedral regions, where each region is assigned to a PWA function describing the local affine dependence of the LPV model coefficients on the scheduling variable. The regression approach consists of two stages. In the first stage, the data samples are processed iteratively, and a Mixed-Integer Quadratic Programming (MIQP) problem is solved to cluster the scheduling variable observations and simultaneously fit the model parameters to the training data, within a relatively short moving-horizon window of the past. At the second stage, the polyhedral partition of the scheduling-variable space is computed by separating the estimated clusters through linear multi-category discrimination.