TY - JOUR

T1 - State-space LPV model identification using kernelized machine learning

AU - Rizvi, S.Z.

AU - Velni, J.M.

AU - Abbasi, F.

AU - Tóth, R.

AU - Meskin, N.

PY - 2018/2/1

Y1 - 2018/2/1

N2 - This paper presents a nonparametric method for identification of MIMO linear parameter-varying (LPV) models in state-space form. The states are first estimated up to a similarity transformation via a nonlinear canonical correlation analysis (CCA) operating in a reproducing kernel Hilbert space (RKHS). This enables to reconstruct a minimal-dimensional inference between past and future input, output and scheduling variables, making it possible to estimate a state sequence consistent with the data. Once the states are estimated, a least-squares support vector machine (LS-SVM)-based identification scheme is formulated, allowing to capture the dependency structure of the matrices of the estimated state-space model on the scheduling variables without requiring an explicit declaration of these often unknown dependencies; instead, it only requires the selection of nonlinear kernel functions and the tuning of the associated hyper-parameters.

AB - This paper presents a nonparametric method for identification of MIMO linear parameter-varying (LPV) models in state-space form. The states are first estimated up to a similarity transformation via a nonlinear canonical correlation analysis (CCA) operating in a reproducing kernel Hilbert space (RKHS). This enables to reconstruct a minimal-dimensional inference between past and future input, output and scheduling variables, making it possible to estimate a state sequence consistent with the data. Once the states are estimated, a least-squares support vector machine (LS-SVM)-based identification scheme is formulated, allowing to capture the dependency structure of the matrices of the estimated state-space model on the scheduling variables without requiring an explicit declaration of these often unknown dependencies; instead, it only requires the selection of nonlinear kernel functions and the tuning of the associated hyper-parameters.

KW - Kernels

KW - Linear parameter-varying models

KW - Nonparametric identification

KW - Support vector machines

UR - http://www.scopus.com/inward/record.url?scp=85037524405&partnerID=8YFLogxK

U2 - 10.1016/j.automatica.2017.11.004

DO - 10.1016/j.automatica.2017.11.004

M3 - Article

AN - SCOPUS:85037524405

SN - 0005-1098

VL - 88

SP - 38

EP - 47

JO - Automatica

JF - Automatica

ER -