  We propose a novel sampled-data output-feedback controller for nonlinear
systems of arbitrary relative degree that ensures reference tracking within
prescribed error bounds. We provide explicit bounds on the maximum input signal
and the required uniform sampling time. A key strength of this approach is its
capability to serve as a safety filter for various learning-based controller
designs, enabling the use of learning techniques in safety-critical
applications. We illustrate its versatility by integrating it with two
different controllers: a reinforcement learning controller and a non-parametric
predictive controller based on Willems et al.'s fundamental lemma. Numerical
simulations illustrate effectiveness of the combined controller design.
