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.

翻译：本文提出了一种面向工业基准问题的端到端鲁棒结构/控制优化框架。在空间结构设计中，降低航天器质量对于最小化任务成本及最大化推进剂容量至关重要。然而，轻量化设计会带来更大的结构柔性，并对控制性能产生影响。为此，本文提出了两种优化架构（分布式与整体式）以应对该问题。特别地，利用线性分式变换（LFT）框架，通过引入参数不确定性来形式化地构建这两个优化问题。由于在微重力条件下无法通过地面实验完全验证结构模型，且航天器动力学在任务期间会发生变化（如结构退化与燃料消耗），因此航天器控制设计必须考虑大量不确定性。本文采用双输入双输出端口（TITOP）多体方法，以最小LFT形式构建柔性动力学模型。详细阐述了所提出的两种优化算法，并在欧洲空间局未来探索任务（ENVISION基准问题）上对比了它们的性能。通过两种方法，在满足任务控制性能/稳定性要求及大量不确定性的前提下，实现了质量的显著降低。