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Learning Safe and Optimal Control Strategies for Storm Water Detention Ponds ?

机译：学习风暴水拘留池的安全和最佳控制策略 ？

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Storm water detention ponds are used to manage the discharge of rainfall runoff from urban areas to nearby streams. Their purpose is to reduce the hydraulic impact and sediment loads of the receiving waters. Detention ponds are currently designed based on static controls: the output flow of a pond is capped at a fixed value. This is not optimal with respect to the current infrastructure capacity and for some detention ponds it might even violate current regulations set by the European Water Framework Directive. We apply formal methods to synthesize (i.e., derive automatically) a safe and optimal active controller. We model the storm water detention pond, including the urban catchment area and the rain forecasts, as a hybrid Markov decision process. Subsequently, we use the tool Uppaal Stratego to synthesize a control strategy minimizing the cost related to pollution (optimality) while guaranteeing no emergency overflow of the detention pond (safety). Simulation results for an existing pond show that Uppaal Stratego can learn optimal strategies that prevent emergency overflows, where the current static control is not always able to prevent it. At the same time, our approach can improve sedimentation during low rain periods.
机译：雨水拘留池用于管理城市地区降雨径流的排放到附近的溪流。他们的目的是减少接收水域的液压冲击和沉积物。拘留池目前基于静态控制设计：池塘的输出流量被固定在固定值。这对当前基础设施能力和一些拘留池来说并不是最佳，这可能甚至可能违反欧洲水框架指令所设定的当前规定。我们将正式方法应用于合成（即，自动推导）安全和最佳的活动控制器。我们模拟了风暴水拘留池，包括城市集水区和雨预测，作为混合马尔可夫决策过程。随后，我们使用工具UPPAAL STRATEGO来综合控制策略最小化与污染（最优性）相关的成本，同时保证拘留池（安全）的紧急溢出。现有池塘的仿真结果表明，UPPAAL STRATEGO可以学习防止紧急溢出的最佳策略，目前的静态控制并不总是能够防止它。与此同时，我们的方法可以在低雨期间提高沉降。

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