  This paper presents a new experiment design method for data-driven modeling
and control. The idea is to select inputs online (using past input/output
data), leading to desirable rank properties of data Hankel matrices. In
comparison to the classical persistency of excitation condition, this online
approach requires less data samples and is even shown to be completely sample
efficient.
