走廊模型预测控制用于轨道结构的多智能体检查
Corridor MPC for Multi-Agent Inspection of Orbiting Structures
AI总结：
本文扩展走廊模型预测控制至高阶分布式系统，利用高阶控制屏障函数约束确保智能体在轨检查时保持安全走廊，并通过数值测试和ISS场景仿真验证了方案的可行性与安全性。
摘要：
在这项工作中，我们提出了先前引入的走廊模型预测控制方案的一种扩展，用于高阶和分布式系统，并应用于在轨检查。为此，我们利用高阶控制屏障函数（HOCBF）约束作为一种合适的控制方法，以保持编队中的每个智能体在其参考轨迹的安全走廊内。所设计的MPC方案的递归可行性通过数值测试验证，同时对经典HOCBF约束定义进行了适当的修改，以确保在采样时间和连续时间内的安全性。所设计的控制器通过国际空间站真实检查场景的计算机仿真进行了验证。
英文摘要：
In this work, we propose an extension of the previously introduced Corridor Model Predictive Control scheme for high-order and distributed systems, with an application for on-orbit inspection. To this end, we leverage high order control barrier function (HOCBF) constraints as a suitable control approach to maintain each agent in the formation within a safe corridor from its reference trajectory. The recursive feasibility of the designed MPC scheme is tested numerically, while suitable modifications of the classical HOCBF constraint definition are introduced such that safety is guaranteed both in sampled and continuous time. The designed controller is validated through computer simulation in a realistic inspection scenario of the International Space Station.
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