State Estimation for Closed-Loop LPV System Identification via Kernel Methods⋆

Abstract

This paper proposes a state-estimation method for closed-loop identification of linear-parameter-varying (LPV) systems by extending an approach for linear-time-invariant (LTI) systems and applying kernel canonical correlation analysis (KCCA). The proposed method estimates the state term from the one-step-ahead prediction via a kernel approach. The incomplete Cholesky decomposition (ICD) is introduced to reduce the complexity of the KCCA. A simple numerical simulation shows the effectiveness of the proposed approach.