  This paper considers the general problem of transitioning theoretically safe
controllers to hardware. Concretely, we explore the application of control
barrier functions (CBFs) to sampled-data systems: systems that evolve
continuously but whose control actions are computed in discrete time-steps.
While this model formulation is less commonly used than its continuous
counterpart, it more accurately models the reality of most control systems in
practice, making the safety guarantees more impactful. In this context, we
prove robust set invariance with respect to zero-order hold controllers as well
as state uncertainty, without the need to explicitly compute any control
invariant sets. It is then shown that this formulation can be exploited to
address input delays in this system, with the result being CBF constraints that
are affine in the input. The results are demonstrated in a high-fidelity
simulation of an unstable Segway robotic system in real-time.
