发表机构
University of Cincinnati; Chosun University（辛辛那提大学; 朝鲜大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究针对航天器最终接近阶段的控制问题，构建基于遗传模糊系统的控制器，通过遗传算法训练后，在带干扰的测试环境中验证其可实现追踪器节能到达合作目标。
AI 中文摘要
在轨服务因可延长存在故障组件的航天器运行寿命而备受关注，这需要追踪器执行交会与接近操作以对目标航天器提供服务。本研究构建了基于模糊推理系统的控制器，使追踪器在最终接近阶段到达圆轨道上的合作目标，同时最小化追踪器的能量消耗。采用遗传算法执行的离线训练过程处理追踪器的多个初始相对位置，训练后的控制器在存在干扰的测试环境中得到验证，该环境与训练场景存在差异。
英文摘要
In-space servicing has been receiving great attention to extend the operation of spacecraft with defective components. This requires rendezvous and proximity operations for a chaser to provide service to a target. This work constructs a fuzzy inference system-based controller for the chaser to reach the cooperative target on a circular orbit in the final approach phase while minimizing the energy consumption of the chaser. The offline training process performed by a genetic algorithm deals with multiple initial relative positions of the chaser, and the trained controller is validated using a testing environment with disturbances, which differs from the training scenarios.
Comments10 pages, 6 figures, 2023 33rd AAS/AIAA Space Flight Mechanics Meeting
