Information-Driven Fault Detection and Identification for Multi-Agent Spacecraft Systems: Collaborative On-Orbit Inspection Mission
Akshita Gupta, Arna Bhardwaj, Yashwanth Kumar Nakka, Changrak Choi, and Amir Rahmani
这项工作为在低地球轨道上执行协作检查任务的多航天器系统提供了一个全球对本地的任务感知故障检测和识别(FDI)框架。 检查任务由全球信息驱动的成本功能代表,该功能集成了传感器模型,航天器姿势和任务级信息增益目标。 该公式通过使用相同的成本功能来驱动全球任务分配和地方传感或运动决策,将指导、控制和外国直接投资联系起来。 通过比较预期和观察到的任务指标来实现故障检测,而高阶成本梯度措施可以识别传感器、执行器和状态估算器之间的故障。 自适应阈值机制可捕获时间变化的检查几何形状和动态任务条件。 代表性多航天器检查场景的模拟结果表明了不确定性下的故障定位和分类的可靠性,为弹性自主检查架构提供了统一、信息驱动的基础。
This work presents a global-to-local, task-aware fault detection and identification (FDI) framework for multi-spacecraft systems conducting collaborative inspection missions in low Earth orbit. The inspection task is represented by a global information-driven cost functional that integrates the sensor model, spacecraft poses, and mission-level information-gain objectives. This formulation links guidance, control, and FDI by using the same cost function to drive both global task allocation and lo...
