  Practical application of H[infinity] robust control relies on system
identification of a valid model-set, described by a linear system in feedback
with a stable norm-bounded uncertainty, which must explains all possible (or at
least all previously measured) behavior for the control plant. Such models can
be viewed as norm-bounded inclusions in the frequency domain, and this note
introduces the "Quadric Inclusion Program" that can identify inclusions from
input--output data as a convex problem. We prove several key properties of this
algorithm and give a geometric interpretation for its behavior. While we stress
that the inclusion fitting is outlier-sensitive by design, we offer a method to
mitigate the effect of measurement noise. We apply this method to robustly
approximate simulated frequency domain data using orthonormal basis functions.
The result compares favorably with a least squares approach that satisfies the
same data inclusion requirements.
