Data selection and data-enabled predictive control for a fuel cell system

Abstract

Data-enabled predictive control is applied for power tracking of a fuel cell system and algorithmic aspects are investigated. For the system realization, the column subset selection algorithms norm sampling, iterative norm sampling, leverage score sampling, and selection based on the strong rank-revealing QR factorization are compared. Regularization using the 1-norm and the squared 2-norm on the column combination vector g is evaluated in terms of runtime and closed-loop results. The results indicate that column selection based on a strong rank-revealing QR (sRRQR) decomposition and leverage score sampling lead to consistently small closed-loop costs even with comparatively few columns in the data matrix resulting in reduced computation time. The closed-loop performance with 1-norm and squared 2-norm is similar, with lower turnaround times for the latter.