Identification of non-uniformly sampled-data systems with asynchronous input and output data

摘要

This paper considers the identification problem of non-uniformly sampled-data (NUSD) systems with asynchronous input and output data. By using the lifting technique, the lifted transfer function (L-TF) model of the asynchronous NUSD systems is derived. Furthermore, an auxiliary model based recursive least squares (AM-RLS) algorithm is developed to directly identify the L-TF model. In order to avoid the causality constraint problem and improve the computational efficiency, a coupled AM-RLS algorithm is proposed to identify the subsystems of the L-TF model. The effectiveness of the proposed identification algorithms is validated by two simulation examples. (C) 2017 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.