Recursive Least-Squares Estimation for Hammerstein Nonlinear Systems with Nonuniform Sampling

Abstract EN

This paper focuses on the identification problem of Hammerstein nonlinear systems with nonuniform sampling.

Using the key-term separation principle, we present a discrete identification model with nonuniform sampling input and output data based on the frame period.

To estimate parameters of the presented model, an auxiliary model-based recursive least-squares algorithm is derived by replacing the unmeasurable variables in the information vector with their corresponding recursive estimates.

The simulation results show the effectiveness of the proposed algorithm.