Work

Regularized Moving-Horizon PWA Regression for LPV System Identification

Year: 2018

Type: article

Abstract: 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.

Source: IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2018.09.048

Authors: Manas Mejari, Vihangkumar V. Naik, Dario Piga, Alberto Bemporad

Institutions: IMT School for Advanced Studies Lucca, University of Applied Sciences and Arts of Southern Switzerland, Dalle Molle Institute for Artificial Intelligence Research

Language: English

Topic: Control Systems and Identification

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond

OpenAlex ID: W2896830695

FWCI: 0.507

Cites: 14

Cited by: 5

Related to: 10