Identification of non-uniformly periodically sampled-data systems based on auxiliary model and singular value decomposition

The authors state the non-uniformly periodically sampling pattern and derive the state-space models of non-uniformly periodically sampled-data systems (NUPSS), and further obtains the corresponding transfer function models. The identification difficulties are that there exist unknown inner variables and unmeasurable noise terms in the information vectors. By means of the auxiliary model method, a recursive least squares algorithm using singular value decomposition (SVD) is presented to confirm the model of NUPSS. The purpose of using SVD is to reduce the computational load of the algorithm and to guarantee the stability of the algorithm using singular value decomposition. An illustrative example is shown to demonstrate the effectiveness and merits of the proposed identification method.