用强化学习训练神经网络，实现卫星自主对接的快速自适应控制。
Neural-based Control for CubeSat Docking Maneuvers
- 通过强化学习训练神经网络，从经验中学习控制策略。
- 6自由度仿真中多次测试均成功完成对接，具备强抗扰能力。
- 适合需要快速响应和自主决策的低轨卫星任务。
近年来，自主交会与对接（RVD）受到广泛关注，以应对航天器动力学变化严苛要求及制导导航控制（GNC）系统的局限性。本文提出一种基于人工神经网络（ANN）的创新方法，利用强化学习（RL）在交会最后阶段实现航天器自主引导与控制。该策略易于在轨实施，通过经验学习控制策略而非依赖预设模型，具备快速适应性和对扰动的鲁棒性。在6自由度（6DoF）环境下开展大量蒙特卡洛仿真以验证方法有效性，并完成硬件测试，证明了部署可行性。研究结果表明，强化学习能有效保障航天器RVD的适应性与效率，为未来任务提供了重要参考。
原文摘要 · Abstract (English)
Autonomous Rendezvous and Docking (RVD) have been extensively studied in recent years, addressing the stringent requirements of spacecraft dynamics variations and the limitations of GNC systems. This paper presents an innovative approach employing Artificial Neural Networks (ANN) trained through Reinforcement Learning (RL) for autonomous spacecraft guidance and control during the final phase of the rendezvous maneuver. The proposed strategy is easily implementable onboard and offers fast adaptability and robustness to disturbances by learning control policies from experience rather than relying on predefined models. Extensive Monte Carlo simulations within a relevant environment are conducted in 6DoF settings to validate our approach, along with hardware tests that demonstrate deployment feasibility. Our findings highlight the efficacy of RL in assuring the adaptability and efficiency of spacecraft RVD, offering insights into future mission expectations.
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