模块化卫星的姿态控制形态与行为协同优化
Morphology and Behavior Co-Optimization of Modular Satellites for Attitude Control
AI总结：
本文提出一种基于梯度的协同优化方法，同时优化模块化卫星的形态与控制，解决传统进化方法计算昂贵且样本效率低的问题，在姿态控制任务中取得更优性能。
摘要：
模块化卫星的出现标志着航天器工程的重大变革，为太空探索引入了灵活性、弹性和可扩展性的新范式。在应对姿态控制等复杂挑战时，卫星的形态架构和控制器对优化性能均至关重要。尽管在最优控制方面已有大量研究，但在开发针对特定任务约束量身定制的模块化卫星优化且实用的组装策略方面仍存在显著空白。这一研究空白主要源于协同优化设计与控制固有的复杂性，该过程以其臭名昭著的双层优化循环而闻名。传统上通过人工进化解决这一问题，即根据单个控制器的适应度优化形态，但这种方法样本效率低且计算成本高。在本文中，我们引入一种新颖的基于梯度的方法，同时优化模块化卫星的形态和控制，提升其在姿态控制任务中的性能和效率。我们的Monte Carlo模拟表明，与基于进化的方法设计的模块化卫星相比，这种协同优化方法带来了更好的任务性能。此外，本研究讨论了未来研究的潜在途径。
英文摘要：
The emergence of modular satellites marks a significant transformation in spacecraft engineering, introducing a new paradigm of flexibility, resilience, and scalability in space exploration endeavors. In addressing complex challenges such as attitude control, both the satellite's morphological architecture and the controller are crucial for optimizing performance. Despite substantial research on optimal control, there remains a significant gap in developing optimized and practical assembly strategies for modular satellites tailored to specific mission constraints. This research gap primarily arises from the inherently complex nature of co-optimizing design and control, a process known for its notorious bi-level optimization loop. Conventionally tackled through artificial evolution, this issue involves optimizing the morphology based on the fitness of individual controllers, which is sample-inefficient and computationally expensive. In this paper, we introduce a novel gradient-based approach to simultaneously optimize both morphology and control for modular satellites, enhancing their performance and efficiency in attitude control missions. Our Monte Carlo simulations demonstrate that this co-optimization approach results in modular satellites with better mission performance compared to those designed by evolution-based approaches. Furthermore, this study discusses potential avenues for future research.
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