Distributed tree-based model predictive control on an open water system

Open water systems are one of the most externally influenced systems due to their size and continuous exposure to uncertain meteorological forces. In this paper we use a stochastic programming approach to control a drainage system in which the weather forecast is modeled as a disturbance tree. A model predictive controller is used to optimize the expected value of the system variables taking into account the disturbance tree. This technique, tree-based model predictive control (TBMPC), is solved in a parallel fashion by means of dual decomposition. In addition, different possibilities are explored to reduce the communicational burden of the parallel algorithm. Finally, the performance of this technique is compared with others such as minmax or multiple model predictive control.