  The paper studies digital redesign of linear time-invariant analog
controllers under intermittent sampling. The sampling pattern is only assumed
to be uniformly bounded, but otherwise irregular and unknown a priori. The
contribution of the paper is twofold. First, it proposes a constructive
algorithm to redesign any analog stabilizing controller so that the closed-loop
stability is preserved. Second, it is shown that when applied to (sub)optimal
$H^2$ and $H^\infty$ controllers, the algorithm produces (sub)optimal
sampled-data solutions under any a priori unknown sampling pattern. The
proposed solutions are analytic, computationally simple, implementable, and
transparent. Transparency pays off in showing the optimality, under a fixed
sampling density, of uniform sampling for both performance measures studied.
