安全且近最优的闸门控制：来自丹麦西海岸的一个案例研究
Safe and Near-Optimal Gate Control: A Case Study from the Danish West Coast
AI总结：
本文通过数字孪生和实时预测学习闸门控制器，确保水位安全同时维持其他性能要求。
摘要：
Ringkoebing Fjord是丹麦西海岸的一个内陆水盆地，通过闸门控制与北海的水交流。目前，人类操作员决定何时及开启多少闸门以控制水位，目标是满足一系列冲突的安全和性能要求，如保持水位在目标范围内、允许海上交通和促进鱼类迁徙。我们使用Uppaal Stratego进行数字孪生，并结合海平面和风速预测，以在线方式学习闸门控制器。我们在不同海平面场景下评估学习的控制器，代表正常潮汐行为、高水位和低水位。我们的评估表明，与基线控制器不同，学习的控制器满足安全要求，同时在其他要求上表现相似。
英文摘要：
Ringkoebing Fjord is an inland water basin on the Danish west coast separated from the North Sea by a set of gates used to control the amount of water entering and leaving the fjord. Currently, human operators decide when and how many gates to open or close for controlling the fjord's water level, with the goal to satisfy a range of conflicting safety and performance requirements such as keeping the water level in a target range, allowing maritime traffic, and enabling fish migration. Uppaal Stratego. We then use this digital twin along with forecasts of the sea level and the wind speed to learn a gate controller in an online fashion. We evaluate the learned controllers under different sea-level scenarios, representing normal tidal behavior, high waters, and low waters. Our evaluation demonstrates that, unlike a baseline controller, the learned controllers satisfy the safety requirements, while performing similarly regarding the other requirements.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
