Robust control-oriented system identification is considered for discrete and continuous time systems. It is shown here how largely to eliminate the small noise-to-signal ratio and high computational cost issues which have been claimed to limit the usefulness of the new theory. This is achieved through a linear data preprocessing and compression step to obtain small nonlinear constrained optimization problems which are easy to solve even in real-time. In particular, a method based on sample correlation quantities and the Chebyshev criterion is considered. Furthermore, worst-case L-1 identification is studied for BIBO stable linear continuous time systems. The existence of robustly convergent identification algorithms is established in the Banach space L-1 in a constructive way. The topic of L-1 identification of continuous time systems is nicely motivated by the recently developed robust sampled-data control theory of continuous-time systems.