Full-parameter constrained parsimonious subspace identification with steady-state information for DC–DC converters

A full-parameter constrained parsimonious subspace identification method that incorporates the steady-state a priori information of the system is proposed to model the DC–DC converters. A parsimonious model with fewer parameters is used to represent the system, and then an optimal weighted methods is used to estimate the system parameters matrices by taking into account both dynamical data and steady-state data. Compared with traditional data-driven methods for DC–DC converters, the subspace-based method can simultaneously estimate model structure and parameter with appropriate computational complexity. Moreover, compared with the traditional full-parameter constrained subspace approach, the proposed algorithm can accurately estimate the system parameters with a smaller variance. The experimental results on a DC–DC synchronous buck converter verify the effectiveness and superiority of the proposed method.