  We present a data-based approach to design event-triggered state-feedback
controllers for unknown continuous-time linear systems affected by
disturbances. By an event, we mean state measurements transmission from the
sensors to the controller over a digital network. By exploiting a sufficiently
rich finite set of noisy state measurements and inputs collected off-line, we
first design a data-driven state-feedback controller to ensure an
input-to-state stability property for the closed-loop system ignoring the
network. We then take into account sampling induced by the network and we
present robust data-driven triggering strategies to (approximately) preserve
this stability property. The approach is general in the sense that it allows
deriving data-based versions of various popular triggering rules of the
literature. In all cases, the designed transmission policies ensure the
existence of a (global) strictly positive minimum inter-event times thereby
excluding Zeno phenomenon despite disturbances. These results can be viewed as
a step towards plug-and-play control for networked control systems, i.e.,
mechanisms that automatically learn to control and to communicate over a
network.
