  The exact pole placement problem concerns computing a feedback gain that will
assign the poles of a system, controlled via static state feedback, at a set of
pre-specified locations. This is a classic problem in feedback control and
numerous methodologies have been proposed in the literature for cases where a
model of the system to control is available. In this paper, we study the
problem of computing feedback gains for pole placement (and, more generally,
eigenstructure assignment) directly from experimental data. Interestingly, we
show that the closed-loop poles can be placed exactly at arbitrary locations
without relying on any model description but by using only finite-length
trajectories generated by the open-loop system. In turn, these findings imply
that classical control objectives, such as feedback stabilization or meeting
transient performance specifications, can be achieved without first identifying
a system model. Numerical experiments demonstrate the benefits of the
data-driven pole-placement approach as compared to its model-based counterpart.
