论文标题
随机模型预测控制和下水道网络
Stochastic Model Predictive Control and Sewer Networks
论文作者
论文摘要
在这项工作中，介绍了对经典确定性模型预测控制（MPC）的偶然受限模型预测控制（CC-MPC）的评估。该评估的重点是在存在不确定性时避免堰溢。此外，CC-MPC的设计配方与MPC的设计进行了比较。为了进行评估，使用了巴塞罗那下水道网络案例研究的简化模型。我们的比较表明，对于不确定流入的下水道系统，CC-MPC比依靠确定性的MPC提供了更好的统计保证，可以避免堰堰溢出。对于CC-MPC，根据分析结果，在不可行的优化程序中，一个简单的备份策略也很明显。
In this work, an evaluation of Chance-Constrained Model Predictive Control (CC-MPC) in sewer systems over the use of the classical deterministic Model Predictive Control (MPC) is presented. The focus of this evaluation is on the avoidance of weir overflow when uncertainty is present. Furthermore, the design formulation of CC-MPC is presented with a comparison to the design of MPC. For the evaluation, a simplified model of the Barcelona sewer network case study is utilized. Our comparison shows that for sewer systems with uncertain inflows, a CC-MPC allows for better statistical guarantees for avoiding weir overflow, than relying on a deterministic MPC. A simple back-up strategy in case of infeasible optimization program was also apparent for the CC-MPC based on the results of the analysis.
