Online Inductance and Capacitance Identification Based on Variable Forgetting Factor Recursive Least-Squares Algorithm for Boost Converter

Abstract

The control performance of boost converter suffers from the variations of important component parameters, such as inductance and capacitance. In this paper, an online inductance and capacitance identification based on variable forgetting factor recursive least-squares (VFF-RLS) algorithm for boost converter is proposed. First, accurate inductance and capacitance identification models and the RLS algorithm are introduced. In order to balance the steady-state identification accuracy and parameter tracking ability, a forgetting factor control technique is investigated. By recovering system noise in the error signal of the algorithm, the value of forgetting factor is dynamically calculated. In addition, since the sampling rate is much lower than the existing identification methods, the proposed algorithm is practical for low-cost applications. Finally, the effectiveness of the proposed algorithm is verified by experiment. The experiment results show that the algorithm has good performance in tracking inductance and capacitance variations.