Joint Identification of a Valve Stiction Hammerstein System with Unknown Time Delay Based on Model Reconstruction

To achieve modeling and identification for systems with valve stiction nonlinearities, unlike existing methods that mostly rely on numerical optimization and assume known time delays, a model-reconstruction-based joint identification method is proposed for valve stiction Hammerstein systems with an unknown time delay. First, an enhanced Li stiction model is developed and derived in a discrete recursive form, and then embedded into an Autoregressive moving average with exogenous input model to construct a parameter-separated stiction Hammerstein identification model. Second, an extended model is formed by augmenting the parameter vector based on a redundant-rule scheme, so as to eliminate the effect of the unknown delay on the model structure. Finally, data filtering and hierarchical identification strategies are introduced, and a filtered rolling-window hierarchical forgetting-gradient recursive subalgorithm is designed to estimate the model parameters; the time delay is determined via a discrete search and redundant parameters are removed. Simulation and experimental results demonstrate good estimation accuracy and predictive performance, validating the effectiveness of the proposed method.