发表机构
Peking University（北京大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文量化风场保真度对四旋翼强化学习控制器鲁棒性的影响，发现离散阵风域随机化训练最有效，且控制权限而非风真实性限制鲁棒性。
AI 中文摘要
强化学习四旋翼控制器通常在简化风模型下训练，然而风场保真度（而非风强度）对策略鲁棒性的影响尚未被量化。本文在完整的跨保真度训练-测试评估中，对近端策略优化（PPO）智能体比较了五种扰动保真度级别，从无风飞行和离散1-余弦阵风，经统计湍流和合成相干结构，到大气边界层的大涡模拟场，并以级联PID和几何SE(3)控制器作为无需训练的参考，覆盖0-12米/秒的风速扫描。在进行任何控制器比较之前，所有扰动数据均经过验证：每个合成生成器都针对其解析或认证标准参考进行定量检查，大涡模拟场则针对施加的对数律进行验证。在悬停的竞速级四旋翼上，训练-测试矩阵异常平坦，且最廉价的结构化训练风（即离散阵风域随机化）在每个测试列中均排名第一，这一排名在风敏感的27克平台上得到复现；一次调参的几何控制器以零坠机率包围了学习到的PPO策略。机制诊断表明，限制鲁棒性的是控制权限而非风的真实性，因此风保真度投资应与平台风敏感性成比例。
英文摘要
Reinforcement-learning quadrotor controllers are usually trained under simplified wind models, yet the impact of wind-field fidelity, as opposed to magnitude, on policy robustness remains unquantified. This paper compares five disturbance-fidelity levels, from wind-free flight and discrete 1-cosine gusts through statistical turbulence and synthetic coherent structures to large-eddy-simulation fields of the atmospheric boundary layer, in a full cross-fidelity train test evaluation of proximal policy optimization (PPO) agents, with cascaded PID and geometric SE(3) controllers as training-free references, over a 0-12 m/s wind sweep. Before any controller comparison is made, all disturbance data are validated: every synthetic generator is checked quantitatively against its analytical or certification-standard reference, and the large-eddy-simulation fields against the imposed log law. On a racing-class quadrotor in hover, the train test matrix is remarkably flat, and the cheapest structured training wind, which is discrete-gust domain randomization, ranks first in every test column, a ranking replicated on a wind-sensitive 27 g platform; a once-tuned geometric controller brackets the learned PPO policies at zero crash rate. Mechanism diagnostics show that control authority, not wind realism, bounds robustness, so wind-fidelity investment should scale with platform wind sensitivity.
