A Bias-Correction Approach for the Identification of Piecewise Affine Output-Error Models. Issue 2 (2020)

• Record Type: Journal Article
• Authors: Mejari, Manas; Breschi, Valentina; Naik, Vihangkumar V.; Piga, Dario
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 1096
• Page End: 1101
• Publication Date: 2020
• Subjects: Hybrid systems -- PWA regression -- bias corrected least-squares -- output-error models; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.1307
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: The paper presents an algorithm for the identification of PieceWise Affine Output-Error (PWA-OE) models, which involves the estimation of the parameters defining affine submodels as well as a partition of the regressor space. For the estimation of affine submodel parameters, a bias-correction scheme is presented to correct the bias in the least squares estimates which is caused by the output-error noise structure. The obtained bias-corrected estimates are proven to be consistent under suitable assumptions. The bias-correction method is then combined with a recursive estimation algorithm for clustering the regressors. These clusters are used to compute a partition of the regressor space by employing linear multi-category discrimination. The effectiveness of the proposed methodology is demonstrated via a simulation case study.