System Identification Using Kalman Filters

Abstract

AbstractThe present study focuses on Model Order Reduction (MOR) methods of non-intrusive nature that can be seen as belonging to the category of system identification techniques. Indeed, whereas the system to analyze is considered as a black box, the accurate modeling of the relationship between its input and output is the aim of the proposed techniques. In this framework, the paper deals with two different methodologies for the system identification of thermal problems. The first identifies a linear thermal system by means of an Extended Kalman Filter (EKF). The approach starts from an a priori guessed analytical model whose expression is assumed to describe appropriately the response of the system to identify. Then, the EKF is used for estimating the model transient states and parameters. However, this methodology is not extended to the processing of nonlinear systems due to the difficulty related to the analytical model construction step. Therefore, a second approach is presented, based on an Unscented Kalman Filter (UKF). Finally, a Finite Element (FE) model is used as a reference, and the good agreement between the FE results and the responses produced by the EKF and UKF methods in the linear case fully illustrate their interest.KeywordsModel Order Reduction (MOR)System identificationExtended Kalman Filter (EKF)Unscented Kalman Filter (UKF)Finite Element (FE)