Robust extended recursive least squares identification algorithm for Hammerstein systems with dynamic disturbances

Abstract

This paper derives an identification algorithm for Hammerstein nonlinear systems with dynamic disturbances and measurement noise. The dynamic disturbance is viewed as a time-varying sequence to be estimated and its model structure and excitation signal are not considered. By extending the parameter and information vector, an extended recursive least squares algorithm is proposed first time to identify recursively both the system parameters and dynamic disturbance of a Hammerstein output error model. By constructing matrix forgetting factor, the dot product operation is used to update covariance matrix, which improves the estimation accuracy of time-invariant system parameters and the tracking performance of dynamic disturbance. The auxiliary model technique ensures that consistent estimation of model parameters can be obtained. The adaptive forgetting factor improves the convergence rate of the algorithm under finite sampling data. Numerical example with Monte-Carlo simulation test is used to verify the superiority of the proposed algorithm.