用强化学习优化丹麦西海岸水闸控制，兼顾安全与运行效率
Safe and Near-Optimal Gate Control: A Case Study from the Danish West Coast
- 构建水闸数字孪生模型，结合海平面和风速预测在线学习控制器
- 在不同潮汐场景下均满足安全水位要求，性能接近基准方案
- 适合水利管理、智能调度系统研究者参考
Ringkoebing Fjord 是丹麦西海岸的一个内海盆地，由一组水闸与北海隔开，用于调控进出峡湾的水量。当前由人工操作决定何时开启或关闭水闸，目标是满足多重冲突的需求：保持水位在目标范围、保障航运通行、促进鱼类洄游。本文基于 Uppaal Stratego 构建该系统的数字孪生模型，并利用海平面与风速预报，在线学习水闸控制策略。在正常潮汐、高潮和低潮三种场景下评估所学控制器，结果表明，相较于基线方案，学习到的控制器能可靠满足安全要求，同时在其他性能指标上表现相当。
原文摘要 · Abstract (English)
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.
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