  We present a novel framework for transferring the knowledge from one system
(source) to design a stabilizing controller for a second system (target). Our
motivation stems from the hypothesis that abundant data can be collected from
the source system, whereas the data from the target system is scarce. We
consider both cases where data collected from the source system is noiseless
and noisy. For each case, by leveraging the data collected from the source
system and a priori knowledge on the maximum distance of the two systems, we
find a suitable, and relatively small, compact set of systems that contains the
actual target system, and then provide a controller that stabilizes the compact
set. In particular, the controller can be obtained by solving a set of linear
matrix inequalities (LMIs). Feasibility of those LMIs is discussed in details.
We complement our theoretical findings by two numerical case studies of
low-order and high-order systems.
