发表机构
Embry–Riddle Aeronautical University（安柏瑞德航空航天大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究多智能体对抗场景下航天器自主交会及接近操作问题，提出结合约束滚动时域MPC与数据驱动监督调整层的框架，经蒙特卡罗模拟评估，该框架相比固定参数MPC有更好性能，为自适应航天器RPO提供模块化、可解释基础。
AI 中文摘要
在对抗性轨道环境中的自主交会和接近操作（RPO）需要能在动态变化的交互条件下平衡目标追踪、安全保障和实时适应性的制导架构。基于学习的方法虽有前景，但在安全关键的轨道机器人技术中的应用受限于可解释性、鲁棒性和约束感知等问题。本文提出了一种用于多智能体对抗场景下自主航天器RPO的自适应模型预测控制（MPC）框架。该框架将约束滚动时域MPC公式与数据驱动的监督调整层相结合，通过离线闭环评估和在线交互几何来调整控制器参数。相对运动遵循Clohessy-Wiltshire（CW）动力学，实现高效计算的有限时域预测和实时二次优化。MPC公式纳入了执行器限制、预测禁区约束、松弛变量可行性处理和可选的控制障碍函数（CBF）安全过滤。自适应层修改可解释的MPC参数，而非直接生成推力命令。通过蒙特卡罗模拟在官方Kerbal太空计划微分博弈（KSPDG）捕获卫星环境中对框架进行评估，结果表明与固定参数MPC相比，具有更好的闭环鲁棒性、自适应机动行为和交会性能，同时保持安全感知操作和实时可行性，为自适应航天器RPO提供了模块化、可解释的基础。
英文摘要
Autonomous rendezvous and proximity operations (RPO) in adversarial orbital environments require guidance architectures balancing target pursuit, safety preservation, and real-time adaptability under dynamically evolving interaction conditions. Although learning-based approaches show promise, their application to safety-critical orbital robotics remains limited by concerns regarding interpretability, robustness, and constraint awareness. This work presents an adaptive Model Predictive Control (MPC) framework for autonomous spacecraft RPO in multi-agent adversarial scenarios. The proposed architecture combines a constrained receding-horizon MPC formulation with a data-driven supervisory tuning layer that adjusts controller parameters from offline closed-loop evaluation and online interaction geometry. Relative motion follows Clohessy-Wiltshire (CW) dynamics, enabling computationally efficient finite-horizon prediction and real-time quadratic optimization. The MPC formulation incorporates actuator limits, predictive keep-out-zone constraints, slack-variable feasibility handling, and optional Control Barrier Function (CBF) safety filtering. Rather than generating thrust commands directly, the adaptive layer modifies interpretable MPC parameters, including tracking weights, safety penalties, minimum-separation objectives, and keep-out-zone objectives. The framework was evaluated in the official Kerbal Space Program Differential Game (KSPDG) Capture-the-Satellite environment through Monte Carlo simulations. Results demonstrate improved closed-loop robustness, adaptive maneuvering behavior, and rendezvous performance compared with fixed-parameter MPC while preserving safety-aware operation and real-time feasibility, providing a modular, interpretable foundation for adaptive spacecraft RPO.
Comments34 pages, 7 figures
