Gradient-based iterative identification for nonuniform sampling output error systems

Abstract

The state-space models are derived for nonuniformly sampled data systems and the corresponding transfer function models are obtained. Since the information vector in the identification model contains unmeasurable true outputs, the conventional stochastic gradient algorithm cannot be applied. In order to overcome this difficulty, the gradient-based iterative identification algorithms are presented by replacing the unmeasurable variables with their iterative estimates at the previous step. The simulation results show that the proposed algorithm has high estimation accuracy compared with the auxiliary model-based stochastic gradient algorithm.