  This paper presents a robust data-driven controller design based on the noisy
input-output data without assumptions on the statistical properties of the
noises. We start with the direct data-representation of system models that take
elements from behavioral system theory, followed by analyses of the upper bound
of the "modeling" error with the data representation with presence of noises.
Some pre-conditioning methods are put into the context based on how the derived
bound is structured. We lastly leverage the upper bound to develop robust
controllers that ride through the data noises.
