Decomposition-based recursive least-squares parameter estimation algorithm for Wiener-Hammerstein systems with dead-zone nonlinearity

In this paper, a decomposition-based recursive least-squares algorithm is proposed for the parameter estimation of Wiener-Hammerstein systems with dead-zone. Based on a smooth parameterisation of the dead-zone nonlinearity, the Wiener-Hammerstein systems with dead-zone can be transformed into a particular model where the parameter vector involves the least number of parameters needed for the identification model description by using the key-term separation principle. On the basis of the particular model, the hierarchical identification principle is presented to decompose the particular model into two identification subsystems, whose parameters are estimated by using a recursive least squares and the auxiliary model method. Furthermore, the convergence analysis of the proposed algorithm ensures that the estimated parameters convergence to their true values. Compared with recursive least squares algorithm and multi-innovation least-squares, the proposed algorithm can avoid the redundant parameters estimation, and meanwhile reduce the computational burden. Numerical examples and experiment are carried out to illustrate the validity of the proposed algorithm.