用强化学习让风筝在乱风中高效发电，不依赖精确模型。
Harvesting energy from turbulent winds with Reinforcement Learning
- 用强化学习自动控制风筝路径，无需预设复杂模型。
- 在湍流环境中仍能有效捕获能量，仅需风筝的局部速度和姿态信息。
- 适合想突破传统控制限制的能源与智能系统研究者。
空中风能（AWE）是一种新兴技术，旨在利用高空风能，解决传统风力涡轮机的若干局限性。AWE 依赖于系留地面站的飞行装置（如滑翔机或风筝），通过风力驱动其运动，并借助发电机将机械能转化为电能。这类系统通常通过操控风筝沿预设路径飞行来实现最优能量捕获，常用方法如模型预测控制（MPC）。然而，这些方法严重依赖具体模型，在不可预测的湍流边界层中难以泛化。本文探索用强化学习（RL）替代传统控制策略的可行性。与传统方法不同，RL 不需要预先建模，对环境变化和不确定性具有更强鲁棒性。在复杂模拟环境中的实验结果表明，使用 RL 训练的 AWE 智能体可在湍流条件下有效提取能量，仅依赖风筝相对于风的局部姿态和速度信息即可实现高效运行。
原文摘要 · Abstract (English)
Airborne Wind Energy (AWE) is an emerging technology designed to harness the power of high-altitude winds, offering a solution to several limitations of conventional wind turbines. AWE is based on flying devices (usually gliders or kites) that, tethered to a ground station and driven by the wind, convert its mechanical energy into electrical energy by means of a generator. Such systems are usually controlled by manoeuvering the kite so as to follow a predefined path prescribed by optimal control techniques, such as model-predictive control. These methods are strongly dependent on the specific model at use and difficult to generalize, especially in unpredictable conditions such as the turbulent atmospheric boundary layer. Our aim is to explore the possibility of replacing these techniques with an approach based on Reinforcement Learning (RL). Unlike traditional methods, RL does not require a predefined model, making it robust to variability and uncertainty. Our experimental results in complex simulated environments demonstrate that AWE agents trained with RL can effectively extract energy from turbulent flows, relying on minimal local information about the kite orientation and speed relative to the wind.
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