Parameter estimation for pseudo‐linear systems using the auxiliary model and the decomposition technique

This study focuses on the parameter identification problems of pseudo‐linear systems. The main goal is to present recursive least squares (RLS) estimation methods based on the auxiliary model identification idea and the decomposition technique. First, an auxiliary model‐based RLS algorithm is given as a comparison. Second, to improve the computation efficiency, a decomposition‐based RLS algorithm is presented. Then for the system identification with missing data, an interval‐varying RLS algorithm is derived for estimating the system parameters. Furthermore, this study uses the decomposition technique to reduce the computational cost in the interval‐varying RLS algorithm and introduces the forgetting factors to track the time‐varying parameters. The simulation results show that the proposed algorithms can work well.