  Robust control theory has been successfully applied to numerous real-world
problems using a small set of devices called {\it controllers}. However, the
real systems represented by networks contain unreliable components and modern
robust control engineering has not addressed the problem of structural changes
on a large network. Here, we introduce the concept of structurally robust
control of complex networks and provide a concrete example using an algorithmic
framework that is widely applied in engineering. The developed analytical
tools, computer simulations and real network analyses lead herein to the
discovery that robust control can be achieved in scale-free networks with
exactly the same order of controllers required in a standard non-robust
configuration by adjusting only the minimum degree. The presented methodology
also addresses the probabilistic failure of links in real systems, such as
neural synaptic unreliability in {\it C. elegans}, and suggests a new direction
to pursue in studies of complex networks in which control theory has a role.
