Prediction-error identification of LPV systems: a nonparametric Gaussian regression approach

DOI10.1016/j.automatica.2018.07.032zbMath1406.93356OpenAlexW2893191621WikidataQ129369603 ScholiaQ129369603MaRDI QIDQ1716507

Ioannis Proimadis, Mohamed Abdelmonim Hassan Darwish, Gianluigi Pillonetto, Pepijn Bastiaan Cox, Roland Tóth

Publication date: 5 February 2019

Published in: Automatica (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.automatica.2018.07.032

zbMATH Keywords

system identificationregularizationmachine learningreproducing kernel Hilbert spacelinear parameter-varying systemsgaussian processesBayesian identificationbox-Jenkins modelsprediction-error identification

Mathematics Subject Classification ID

Bayesian inference (62F15) Applications of statistics in engineering and industry; control charts (62P30) Multivariable systems, multidimensional control systems (93C35) Estimation and detection in stochastic control theory (93E10) Linear systems in control theory (93C05) Asymptotic stability in control theory (93D20) Identification in stochastic control theory (93E12) Stochastic stability in control theory (93E15)

Related Items (4)

Nonparametric identification based on Gaussian process regression for distributed parameter systems ⋮ Introducing system identification strategy into model predictive control ⋮ Sparse RKHS estimation via globally convex optimization and its application in LPV-IO identification ⋮ Kernel-based identification of asymptotically stable continuous-time linear dynamical systems

Uses Software

• ElemStatLearn

Cites Work

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