AI 中文总结
研究针对气动弹性系统，提出基于深度强化学习的无模型嵌套协同设计框架，通过近端策略优化训练控制策略，外环更新设计参数分布。在三个案例研究中评估，结果显示该框架能集中设计搜索，优于随机策略，奖励塑造有助于稳定学习，联合解决多方面问题。
AI 中文摘要
控制协同设计将物理系统及其控制器一起考虑，能揭示并利用系统设计与控制间的强耦合，这在气动弹性飞行系统中尤为重要。本文提出一种基于深度强化学习的无模型嵌套协同设计框架用于气动弹性系统，通过近端策略优化训练设计条件控制策略，外环更新候选设计参数分布。在三个复杂度递增的案例研究中评估该方法，结果表明框架能将设计搜索集中到高性能区域，优于随机采样设计训练的策略，奖励塑造在部分观测和随机环境中对稳定学习起重要作用，在最终滑翔机案例中联合解决了机翼设计、飞行控制和任务级行为等问题。
英文摘要
Control co-design considers the physical system and its controller together, enabling the strong coupling between system design and control to be uncovered and exploited. This is especially relevant in aeroelastic flight systems, where structural, aerodynamic, and control design choices jointly determine manoeuvrability and efficiency. This paper presents a model-free nested co-design framework for aeroelastic systems using deep reinforcement learning, in which a design-conditioned control policy is trained with proximal policy optimisation while an outer loop updates a distribution over candidate design parameters. The approach is evaluated on three case studies of increasing complexity: a spring-mass-damper system, a pitch-plunge-flap aerofoil, and a highly flexible high-aspect-ratio glider performing a thermal-soaring mission in a stochastic environment. Across these case studies, the framework is shown to progressively concentrate the design search towards high-performing regions and to outperform policies trained on randomly sampled designs. The results also show that reward shaping plays an important role in enabling stable learning in partially observed and stochastic environments. In the final glider case, the method jointly addresses wing design, flight control, and mission-level behaviour in the presence of aeroelastic coupling and atmospheric uncertainty. These results highlight the potential of model-free co-design for complex aeroelastic systems in which design, control, and mission objectives are tightly coupled.
Comments29 pages, 19 figures, to be published in the conference proceedings of the International Forum on Aeroelasticity and Structural Dynamics 2026
