Identification of Input-Output Nonlinear Block Structure Systems with Dynamic Disturbances

This paper presents the attempt of modeling input-output nonlinear block structure systems with dynamic disturbances and measurement noise from sampled data. A typical Hammerstein-Wiener model is applied to fit and predict the output response of the system. The dynamic disturbance output will be viewed as a time-varying sequence to be estimated. By extending the parameter and information vector, a discrete-time extended recursive least squares algorithm is proposed to identify both the system parameters and dynamic disturbance. A matrix forgetting factor is utilized to improve the estimation accuracy and the tracking performance of the proposed algorithm. By using the auxiliary model technique, the consistent estimation of model parameters can be guaranteed. Moreover, several asymptotic convergence conditions are also derived based on the stochastic statistical theory under persistent excitation condition. Finally, a numerical example with Monte-Carlo simulation is used to illustrate the merit and effectiveness of the proposed algorithm.