  This paper presents an end-to-end framework for robust structure/control
optimization of an industrial benchmark. When dealing with space structures, a
reduction of the spacecraft mass is paramount to minimize the mission cost and
maximize the propellant availability. However, a lighter design comes with a
bigger structural flexibility and the resulting impact on control performance.
Two optimization architectures (distributed and monolithic) are proposed in
order to face this issue. In particular the Linear Fractional Transformation
(LFT) framework is exploited to formally set the two optimization problems by
including parametric uncertainties. Large sets of uncertainties have to be
indeed taken into account in spacecraft control design due to the impossibility
to completely validate structural models in micro-gravity conditions with
on-ground experiments and to the evolution of spacecraft dynamics during the
mission (structure degradation and fuel consumption). In particular the
Two-Input Two-Output Port (TITOP) multi-body approach is used to build the
flexible dynamics in a minimal LFT form. The two proposed optimization
algorithms are detailed and their performance are compared on an ESA future
exploration mission, the ENVISION benchmark. With both approaches, an important
reduction of the mass is obtained by coping with the mission's control
performance/stability requirements and a large set of uncertainties.
