  The fundamental lemma from behavioral systems theory yields a data-driven
non-parametric system representation that has shown great potential for the
data-efficient control of unknown linear and weakly nonlinear systems, even in
the presence of measurement noise. In this work, we strive to extend the
applicability of this paradigm to more strongly nonlinear systems by updating
the system representation during control. Unlike existing approaches, our
method does not impose suitable excitation to the control inputs, but runs as
an observer parallel to the controller. Whenever a rank condition is deemed to
be fulfilled, the system representation is updated using newly available
datapoints. In a reference tracking simulation of a two-link robotic arm, we
showcase the performance of the proposed strategy in a predictive control
framework.
