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In this study, a fixed point iteration‐based subspace identification method is proposed for Hammerstein state‐space systems. The original system is decomposed into two subsystems with fewer parameters based on the hierarchical identification principle. Each subsystem is related directly to either the linear dynamics or the static non‐linearity. A two‐stage least‐squares‐based iterative method is then implemented to separately estimate the coefficients of the non‐linear subsystem and the extended Markov parameters of the linear subsystem. The linear subsystem parameters are extracted from the identified extended Markov parameters using a singular value decomposition based method. Convergence analysis of the proposed method is established using fixed point theory, which shows that the proposed method gives consistent estimates under arbitrary non‐zero initial conditions. Simulation results are included to show the performance of the proposed method. [ABSTRACT FROM AUTHOR]

This study investigates the estimation of continuous‐time Box–Jenkins model parameters from irregularly sampled data. The Box–Jenkins structure has been successful in describing systems subject to coloured noise, since it contains two sub‐models that feature the characteristics of both plant and noise systems. Based on plant‐noise model decomposition, a two‐step iterative procedure is proposed to solve the estimation problem, which consists of an instrumental variable method for the plant model and a prediction error method for the noise model. The proposed method is of low complexity and shows good estimation robustness and accuracy. Implementation issues are discussed to improve the computational efficiency. Numerical examples are presented to demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]