  The structured singular value (SSV), or mu, is used to assess the robust
stability and performance of an uncertain linear time-invariant system.
Existing algorithms compute upper and lower bounds on the SSV for structured
uncertainties that contain repeated (real or complex) scalars and/or
non-repeated complex full blocks. This paper presents algorithms to compute
bounds on the SSV for the case of repeated complex full blocks. This specific
class of uncertainty is relevant for the input output analysis of many
convective systems, such as fluid flows. Specifically, we present a power
iteration to compute a lower bound on SSV for the case of repeated complex full
blocks. This generalizes existing power iterations for repeated complex scalar
and non-repeated complex full blocks. The upper bound can be formulated as a
semi-definite program (SDP), which we solve using a standard interior-point
method to compute optimal scaling matrices associated with the repeated full
blocks. Our implementation of the method only requires gradient information,
which improves the computational efficiency of the method. Finally, we test our
proposed algorithms on an example model of incompressible fluid flow. The
proposed methods provide less conservative bounds as compared to prior results,
which ignore the repeated full block structure.
