  The design of controllers from data for nonlinear systems is a challenging
problem. In a recent paper, De Persis, Rotulo and Tesi, "Learning controllers
from data via approximate nonlinearity cancellation," IEEE Transactions on
Automatic Control, 2023, a method to learn controllers that make the
closed-loop system stable and dominantly linear was proposed. The approach
leads to a simple solution based on data-dependent semidefinite programs. The
method uses input-state measurements as data, while in a realistic setup it is
more likely that only input-output measurements are available. In this note we
report how the design principle of the above mentioned paper can be adjusted to
deal with input-output data and obtain dynamic output feedback controllers in a
favourable setting.
