Hierarchical recursive least squares algorithms for Hammerstein nonlinear autoregressive output‐error systems
Zhen Kang, Yan Ji, Ximei Liu
Abstract
Summary This article considers the parameter estimation problem of Hammerstein nonlinear autoregressive output‐error systems with autoregressive moving average noises. Applying the key term separation technique, the original system is decomposed into three subsystems: the first subsystem contains the unknown parameters related to the output, the second subsystem contains the unknown parameters related to the input, and the third subsystem contains the unknown parameters related to the noise model. A hierarchical recursive least squares algorithm is proposed based on the hierarchical identification principle for interactively identifying each subsystem. The simulation results confirm that the proposed algorithm is effective in estimating the parameters of Hammerstein nonlinear autoregressive output‐error systems.