A recursive and robust identification method for a class of nonlinear systems

摘要

This paper presents a recursive and robust identification method for a class of non linear systems in presence of unknown but bounded errors. Based on the formulation of the input output difference equation of Multi-Input Single-Output (MISO) Wiener-Hammerstein model, a modified Weighted Extended-Recursive Least squares (WE-RLS) procedure is employed to estimate separately parameters of the linear sub-systems and the static non linear elements related to each input when the disturbances upper bound is supposed to be known. The proposed algorithm is performed by minimizing a cost function where a time varying factor is introduced such as the estimated parameters are consistent with the measurements and the noise constraints and, to improve convergence properties. Sufficient conditions to ensure asymptotic convergence are established. Efficiency of the proposed algorithm is shown through a numerical example of high nonlinearities.