  This paper presents a framework of learning parameter space for
event-triggered control. In particular, our goal is to find a set of parameters
for the event-triggered condition, such that certain specifications on safety
and convergence properties are satisfied. The exploration strategy is based on
the Gaussian process-based active learning, in which, for each iteration, the
parameter with having the largest variance is evaluated. Moreover, we provide a
theoretical analysis, so that the derived parameter space satisfies both
convergence and safety. Finally, a numerical simulation is given to illustrate
the effectiveness of the approach.
