A Kalman pre-filtered IV-based approach to continuous-time Hammerstein-Wiener system identification
Résumé
This paper studies the identification problem for a class of Hammerstein-Wiener continuous- time systems, with a monotonic nonlinear function for the Wiener part. Based on the previously developed simplified refined instrumental variable method, and by making use of an adaptive observer for data filtering, a combined approach, referred to as the Kalman pre-filtered instrumental variable based method, is proposed. By taking the advantages of the two aforementioned methods, the new method is faster and has a naturally stabilized filter, as well as keeps a high estimation accuracy in most cases. Monte Carlo simulation analysis is used to illustrate the performances of the proposed methods.